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<div class="section" id="mllib-dataframe-based">
<h1>MLlib (DataFrame-based)<a class="headerlink" href="#mllib-dataframe-based" title="Permalink to this headline"></a></h1>
<div class="section" id="pipeline-apis">
<h2>Pipeline APIs<a class="headerlink" href="#pipeline-apis" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.Transformer.html#pyspark.ml.Transformer" title="pyspark.ml.Transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformer</span></code></a>()</p></td>
<td><p>Abstract class for transformers that transform one dataset into another.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer" title="pyspark.ml.UnaryTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">UnaryTransformer</span></code></a>()</p></td>
<td><p>Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.Estimator.html#pyspark.ml.Estimator" title="pyspark.ml.Estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Estimator</span></code></a>()</p></td>
<td><p>Abstract class for estimators that fit models to data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.Model.html#pyspark.ml.Model" title="pyspark.ml.Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Model</span></code></a>()</p></td>
<td><p>Abstract class for models that are fitted by estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.Predictor.html#pyspark.ml.Predictor" title="pyspark.ml.Predictor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Predictor</span></code></a>()</p></td>
<td><p>Estimator for prediction tasks (regression and classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.PredictionModel.html#pyspark.ml.PredictionModel" title="pyspark.ml.PredictionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PredictionModel</span></code></a>()</p></td>
<td><p>Model for prediction tasks (regression and classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.Pipeline.html#pyspark.ml.Pipeline" title="pyspark.ml.Pipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Pipeline</span></code></a>(*[, stages])</p></td>
<td><p>A simple pipeline, which acts as an estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.PipelineModel.html#pyspark.ml.PipelineModel" title="pyspark.ml.PipelineModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PipelineModel</span></code></a>(stages)</p></td>
<td><p>Represents a compiled pipeline with transformers and fitted models.</p></td>
</tr>
</tbody>
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</div>
<div class="section" id="parameters">
<h2>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
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<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Param</span></code></a>(parent, name, doc[, typeConverter])</p></td>
<td><p>A param with self-contained documentation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.param.Params.html#pyspark.ml.param.Params" title="pyspark.ml.param.Params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Params</span></code></a>()</p></td>
<td><p>Components that take parameters.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.param.TypeConverters.html#pyspark.ml.param.TypeConverters" title="pyspark.ml.param.TypeConverters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeConverters</span></code></a></p></td>
<td><p>Factory methods for common type conversion functions for <cite>Param.typeConverter</cite>.</p></td>
</tr>
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<div class="section" id="feature">
<h2>Feature<a class="headerlink" href="#feature" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
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<col style="width: 10%" />
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<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Binarizer.html#pyspark.ml.feature.Binarizer" title="pyspark.ml.feature.Binarizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Binarizer</span></code></a>(*[, threshold, inputCol, …])</p></td>
<td><p>Binarize a column of continuous features given a threshold.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.BucketedRandomProjectionLSH.html#pyspark.ml.feature.BucketedRandomProjectionLSH" title="pyspark.ml.feature.BucketedRandomProjectionLSH"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BucketedRandomProjectionLSH</span></code></a>(*[, inputCol, …])</p></td>
<td><p>LSH class for Euclidean distance metrics.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.BucketedRandomProjectionLSHModel.html#pyspark.ml.feature.BucketedRandomProjectionLSHModel" title="pyspark.ml.feature.BucketedRandomProjectionLSHModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BucketedRandomProjectionLSHModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.BucketedRandomProjectionLSH.html#pyspark.ml.feature.BucketedRandomProjectionLSH" title="pyspark.ml.feature.BucketedRandomProjectionLSH"><code class="xref py py-class docutils literal notranslate"><span class="pre">BucketedRandomProjectionLSH</span></code></a>, where multiple random vectors are stored.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Bucketizer.html#pyspark.ml.feature.Bucketizer" title="pyspark.ml.feature.Bucketizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Bucketizer</span></code></a>(*[, splits, inputCol, outputCol, …])</p></td>
<td><p>Maps a column of continuous features to a column of feature buckets.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.ChiSqSelector.html#pyspark.ml.feature.ChiSqSelector" title="pyspark.ml.feature.ChiSqSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqSelector</span></code></a>(*[, numTopFeatures, …])</p></td>
<td><p>Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.ChiSqSelectorModel.html#pyspark.ml.feature.ChiSqSelectorModel" title="pyspark.ml.feature.ChiSqSelectorModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqSelectorModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.ChiSqSelector.html#pyspark.ml.feature.ChiSqSelector" title="pyspark.ml.feature.ChiSqSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">ChiSqSelector</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.CountVectorizer.html#pyspark.ml.feature.CountVectorizer" title="pyspark.ml.feature.CountVectorizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>(*[, minTF, minDF, maxDF, …])</p></td>
<td><p>Extracts a vocabulary from document collections and generates a <a class="reference internal" href="api/pyspark.ml.feature.CountVectorizerModel.html#pyspark.ml.feature.CountVectorizerModel" title="pyspark.ml.feature.CountVectorizerModel"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CountVectorizerModel</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.CountVectorizerModel.html#pyspark.ml.feature.CountVectorizerModel" title="pyspark.ml.feature.CountVectorizerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CountVectorizerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.CountVectorizer.html#pyspark.ml.feature.CountVectorizer" title="pyspark.ml.feature.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.DCT.html#pyspark.ml.feature.DCT" title="pyspark.ml.feature.DCT"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DCT</span></code></a>(*[, inverse, inputCol, outputCol])</p></td>
<td><p>A feature transformer that takes the 1D discrete cosine transform of a real vector.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.ElementwiseProduct.html#pyspark.ml.feature.ElementwiseProduct" title="pyspark.ml.feature.ElementwiseProduct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElementwiseProduct</span></code></a>(*[, scalingVec, …])</p></td>
<td><p>Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided “weight” vector.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.FeatureHasher.html#pyspark.ml.feature.FeatureHasher" title="pyspark.ml.feature.FeatureHasher"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FeatureHasher</span></code></a>(*[, numFeatures, inputCols, …])</p></td>
<td><p>Feature hashing projects a set of categorical or numerical features into a feature vector of specified dimension (typically substantially smaller than that of the original feature space).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.HashingTF.html#pyspark.ml.feature.HashingTF" title="pyspark.ml.feature.HashingTF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HashingTF</span></code></a>(*[, numFeatures, binary, …])</p></td>
<td><p>Maps a sequence of terms to their term frequencies using the hashing trick.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.IDF.html#pyspark.ml.feature.IDF" title="pyspark.ml.feature.IDF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IDF</span></code></a>(*[, minDocFreq, inputCol, outputCol])</p></td>
<td><p>Compute the Inverse Document Frequency (IDF) given a collection of documents.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.IDFModel.html#pyspark.ml.feature.IDFModel" title="pyspark.ml.feature.IDFModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IDFModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.IDF.html#pyspark.ml.feature.IDF" title="pyspark.ml.feature.IDF"><code class="xref py py-class docutils literal notranslate"><span class="pre">IDF</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Imputer.html#pyspark.ml.feature.Imputer" title="pyspark.ml.feature.Imputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Imputer</span></code></a>(*[, strategy, missingValue, …])</p></td>
<td><p>Imputation estimator for completing missing values, using the mean, median or mode of the columns in which the missing values are located.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.ImputerModel.html#pyspark.ml.feature.ImputerModel" title="pyspark.ml.feature.ImputerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImputerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.Imputer.html#pyspark.ml.feature.Imputer" title="pyspark.ml.feature.Imputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Imputer</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.IndexToString.html#pyspark.ml.feature.IndexToString" title="pyspark.ml.feature.IndexToString"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IndexToString</span></code></a>(*[, inputCol, outputCol, labels])</p></td>
<td><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.ml.base.Transformer</span></code> that maps a column of indices back to a new column of corresponding string values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Interaction.html#pyspark.ml.feature.Interaction" title="pyspark.ml.feature.Interaction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Interaction</span></code></a>(*[, inputCols, outputCol])</p></td>
<td><p>Implements the feature interaction transform.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MaxAbsScaler.html#pyspark.ml.feature.MaxAbsScaler" title="pyspark.ml.feature.MaxAbsScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxAbsScaler</span></code></a>(*[, inputCol, outputCol])</p></td>
<td><p>Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MaxAbsScalerModel.html#pyspark.ml.feature.MaxAbsScalerModel" title="pyspark.ml.feature.MaxAbsScalerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxAbsScalerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.MaxAbsScaler.html#pyspark.ml.feature.MaxAbsScaler" title="pyspark.ml.feature.MaxAbsScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxAbsScaler</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MinHashLSH.html#pyspark.ml.feature.MinHashLSH" title="pyspark.ml.feature.MinHashLSH"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinHashLSH</span></code></a>(*[, inputCol, outputCol, seed, …])</p></td>
<td><p>LSH class for Jaccard distance.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MinHashLSHModel.html#pyspark.ml.feature.MinHashLSHModel" title="pyspark.ml.feature.MinHashLSHModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinHashLSHModel</span></code></a>([java_model])</p></td>
<td><p>Model produced by <a class="reference internal" href="api/pyspark.ml.feature.MinHashLSH.html#pyspark.ml.feature.MinHashLSH" title="pyspark.ml.feature.MinHashLSH"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinHashLSH</span></code></a>, where where multiple hash functions are stored.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MinMaxScaler.html#pyspark.ml.feature.MinMaxScaler" title="pyspark.ml.feature.MinMaxScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinMaxScaler</span></code></a>(*[, min, max, inputCol, outputCol])</p></td>
<td><p>Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.MinMaxScalerModel.html#pyspark.ml.feature.MinMaxScalerModel" title="pyspark.ml.feature.MinMaxScalerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinMaxScalerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.MinMaxScaler.html#pyspark.ml.feature.MinMaxScaler" title="pyspark.ml.feature.MinMaxScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinMaxScaler</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.NGram.html#pyspark.ml.feature.NGram" title="pyspark.ml.feature.NGram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NGram</span></code></a>(*[, n, inputCol, outputCol])</p></td>
<td><p>A feature transformer that converts the input array of strings into an array of n-grams.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Normalizer.html#pyspark.ml.feature.Normalizer" title="pyspark.ml.feature.Normalizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Normalizer</span></code></a>(*[, p, inputCol, outputCol])</p></td>
<td><p>Normalize a vector to have unit norm using the given p-norm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.OneHotEncoder.html#pyspark.ml.feature.OneHotEncoder" title="pyspark.ml.feature.OneHotEncoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a>(*[, inputCols, outputCols, …])</p></td>
<td><p>A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.OneHotEncoderModel.html#pyspark.ml.feature.OneHotEncoderModel" title="pyspark.ml.feature.OneHotEncoderModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneHotEncoderModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.OneHotEncoder.html#pyspark.ml.feature.OneHotEncoder" title="pyspark.ml.feature.OneHotEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.PCA.html#pyspark.ml.feature.PCA" title="pyspark.ml.feature.PCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PCA</span></code></a>(*[, k, inputCol, outputCol])</p></td>
<td><p>PCA trains a model to project vectors to a lower dimensional space of the top <code class="xref py py-attr docutils literal notranslate"><span class="pre">k</span></code> principal components.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.PCAModel.html#pyspark.ml.feature.PCAModel" title="pyspark.ml.feature.PCAModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PCAModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.PCA.html#pyspark.ml.feature.PCA" title="pyspark.ml.feature.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCA</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.PolynomialExpansion.html#pyspark.ml.feature.PolynomialExpansion" title="pyspark.ml.feature.PolynomialExpansion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PolynomialExpansion</span></code></a>(*[, degree, inputCol, …])</p></td>
<td><p>Perform feature expansion in a polynomial space.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.QuantileDiscretizer.html#pyspark.ml.feature.QuantileDiscretizer" title="pyspark.ml.feature.QuantileDiscretizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantileDiscretizer</span></code></a>(*[, numBuckets, …])</p></td>
<td><p><a class="reference internal" href="api/pyspark.ml.feature.QuantileDiscretizer.html#pyspark.ml.feature.QuantileDiscretizer" title="pyspark.ml.feature.QuantileDiscretizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantileDiscretizer</span></code></a> takes a column with continuous features and outputs a column with binned categorical features.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.RobustScaler.html#pyspark.ml.feature.RobustScaler" title="pyspark.ml.feature.RobustScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RobustScaler</span></code></a>(*[, lower, upper, …])</p></td>
<td><p>RobustScaler removes the median and scales the data according to the quantile range.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.RobustScalerModel.html#pyspark.ml.feature.RobustScalerModel" title="pyspark.ml.feature.RobustScalerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RobustScalerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.RobustScaler.html#pyspark.ml.feature.RobustScaler" title="pyspark.ml.feature.RobustScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RobustScaler</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.RegexTokenizer.html#pyspark.ml.feature.RegexTokenizer" title="pyspark.ml.feature.RegexTokenizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegexTokenizer</span></code></a>(*[, minTokenLength, gaps, …])</p></td>
<td><p>A regex based tokenizer that extracts tokens either by using the provided regex pattern (in Java dialect) to split the text (default) or repeatedly matching the regex (if gaps is false).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.RFormula.html#pyspark.ml.feature.RFormula" title="pyspark.ml.feature.RFormula"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFormula</span></code></a>(*[, formula, featuresCol, …])</p></td>
<td><p>Implements the transforms required for fitting a dataset against an R model formula.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.RFormulaModel.html#pyspark.ml.feature.RFormulaModel" title="pyspark.ml.feature.RFormulaModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFormulaModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.RFormula.html#pyspark.ml.feature.RFormula" title="pyspark.ml.feature.RFormula"><code class="xref py py-class docutils literal notranslate"><span class="pre">RFormula</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.SQLTransformer.html#pyspark.ml.feature.SQLTransformer" title="pyspark.ml.feature.SQLTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SQLTransformer</span></code></a>(*[, statement])</p></td>
<td><p>Implements the transforms which are defined by SQL statement.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.StandardScaler.html#pyspark.ml.feature.StandardScaler" title="pyspark.ml.feature.StandardScaler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>(*[, withMean, withStd, …])</p></td>
<td><p>Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.StandardScalerModel.html#pyspark.ml.feature.StandardScalerModel" title="pyspark.ml.feature.StandardScalerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StandardScalerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.StandardScaler.html#pyspark.ml.feature.StandardScaler" title="pyspark.ml.feature.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.StopWordsRemover.html#pyspark.ml.feature.StopWordsRemover" title="pyspark.ml.feature.StopWordsRemover"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StopWordsRemover</span></code></a>(*[, inputCol, outputCol, …])</p></td>
<td><p>A feature transformer that filters out stop words from input.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.StringIndexer.html#pyspark.ml.feature.StringIndexer" title="pyspark.ml.feature.StringIndexer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StringIndexer</span></code></a>(*[, inputCol, outputCol, …])</p></td>
<td><p>A label indexer that maps a string column of labels to an ML column of label indices.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.StringIndexerModel.html#pyspark.ml.feature.StringIndexerModel" title="pyspark.ml.feature.StringIndexerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StringIndexerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.StringIndexer.html#pyspark.ml.feature.StringIndexer" title="pyspark.ml.feature.StringIndexer"><code class="xref py py-class docutils literal notranslate"><span class="pre">StringIndexer</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Tokenizer.html#pyspark.ml.feature.Tokenizer" title="pyspark.ml.feature.Tokenizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tokenizer</span></code></a>(*[, inputCol, outputCol])</p></td>
<td><p>A tokenizer that converts the input string to lowercase and then splits it by white spaces.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.UnivariateFeatureSelector.html#pyspark.ml.feature.UnivariateFeatureSelector" title="pyspark.ml.feature.UnivariateFeatureSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">UnivariateFeatureSelector</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>Feature selector based on univariate statistical tests against labels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.UnivariateFeatureSelectorModel.html#pyspark.ml.feature.UnivariateFeatureSelectorModel" title="pyspark.ml.feature.UnivariateFeatureSelectorModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">UnivariateFeatureSelectorModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.UnivariateFeatureSelector.html#pyspark.ml.feature.UnivariateFeatureSelector" title="pyspark.ml.feature.UnivariateFeatureSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">UnivariateFeatureSelector</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VarianceThresholdSelector.html#pyspark.ml.feature.VarianceThresholdSelector" title="pyspark.ml.feature.VarianceThresholdSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VarianceThresholdSelector</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>Feature selector that removes all low-variance features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VarianceThresholdSelectorModel.html#pyspark.ml.feature.VarianceThresholdSelectorModel" title="pyspark.ml.feature.VarianceThresholdSelectorModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VarianceThresholdSelectorModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.VarianceThresholdSelector.html#pyspark.ml.feature.VarianceThresholdSelector" title="pyspark.ml.feature.VarianceThresholdSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">VarianceThresholdSelector</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VectorAssembler.html#pyspark.ml.feature.VectorAssembler" title="pyspark.ml.feature.VectorAssembler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorAssembler</span></code></a>(*[, inputCols, outputCol, …])</p></td>
<td><p>A feature transformer that merges multiple columns into a vector column.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VectorIndexer.html#pyspark.ml.feature.VectorIndexer" title="pyspark.ml.feature.VectorIndexer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorIndexer</span></code></a>(*[, maxCategories, inputCol, …])</p></td>
<td><p>Class for indexing categorical feature columns in a dataset of <cite>Vector</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VectorIndexerModel.html#pyspark.ml.feature.VectorIndexerModel" title="pyspark.ml.feature.VectorIndexerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorIndexerModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.VectorIndexer.html#pyspark.ml.feature.VectorIndexer" title="pyspark.ml.feature.VectorIndexer"><code class="xref py py-class docutils literal notranslate"><span class="pre">VectorIndexer</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VectorSizeHint.html#pyspark.ml.feature.VectorSizeHint" title="pyspark.ml.feature.VectorSizeHint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorSizeHint</span></code></a>(*[, inputCol, size, …])</p></td>
<td><p>A feature transformer that adds size information to the metadata of a vector column.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.VectorSlicer.html#pyspark.ml.feature.VectorSlicer" title="pyspark.ml.feature.VectorSlicer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorSlicer</span></code></a>(*[, inputCol, outputCol, …])</p></td>
<td><p>This class takes a feature vector and outputs a new feature vector with a subarray of the original features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Word2Vec.html#pyspark.ml.feature.Word2Vec" title="pyspark.ml.feature.Word2Vec"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Word2Vec</span></code></a>(*[, vectorSize, minCount, …])</p></td>
<td><p>Word2Vec trains a model of <cite>Map(String, Vector)</cite>, i.e.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.feature.Word2VecModel.html#pyspark.ml.feature.Word2VecModel" title="pyspark.ml.feature.Word2VecModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Word2VecModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.feature.Word2Vec.html#pyspark.ml.feature.Word2Vec" title="pyspark.ml.feature.Word2Vec"><code class="xref py py-class docutils literal notranslate"><span class="pre">Word2Vec</span></code></a>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="classification">
<h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LinearSVC.html#pyspark.ml.classification.LinearSVC" title="pyspark.ml.classification.LinearSVC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearSVC</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>This binary classifier optimizes the Hinge Loss using the OWLQN optimizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LinearSVCModel.html#pyspark.ml.classification.LinearSVCModel" title="pyspark.ml.classification.LinearSVCModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearSVCModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by LinearSVC.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LinearSVCSummary.html#pyspark.ml.classification.LinearSVCSummary" title="pyspark.ml.classification.LinearSVCSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearSVCSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for LinearSVC Results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LinearSVCTrainingSummary.html#pyspark.ml.classification.LinearSVCTrainingSummary" title="pyspark.ml.classification.LinearSVCTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearSVCTrainingSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for LinearSVC Training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LogisticRegression.html#pyspark.ml.classification.LogisticRegression" title="pyspark.ml.classification.LogisticRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>Logistic regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LogisticRegressionModel.html#pyspark.ml.classification.LogisticRegressionModel" title="pyspark.ml.classification.LogisticRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by LogisticRegression.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LogisticRegressionSummary.html#pyspark.ml.classification.LogisticRegressionSummary" title="pyspark.ml.classification.LogisticRegressionSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for Logistic Regression Results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.LogisticRegressionTrainingSummary.html#pyspark.ml.classification.LogisticRegressionTrainingSummary" title="pyspark.ml.classification.LogisticRegressionTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionTrainingSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for multinomial Logistic Regression Training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.BinaryLogisticRegressionSummary.html#pyspark.ml.classification.BinaryLogisticRegressionSummary" title="pyspark.ml.classification.BinaryLogisticRegressionSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryLogisticRegressionSummary</span></code></a>([java_obj])</p></td>
<td><p>Binary Logistic regression results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary.html#pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary" title="pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryLogisticRegressionTrainingSummary</span></code></a>([…])</p></td>
<td><p>Binary Logistic regression training results for a given model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.DecisionTreeClassifier.html#pyspark.ml.classification.DecisionTreeClassifier" title="pyspark.ml.classification.DecisionTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTreeClassifier</span></code></a>(*[, featuresCol, …])</p></td>
<td><p><a class="reference external" href="http://en.wikipedia.org/wiki/Decision_tree_learning">Decision tree</a> learning algorithm for classification.It supports both binary and multiclass labels, as well as both continuous and categorical features..</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.DecisionTreeClassificationModel.html#pyspark.ml.classification.DecisionTreeClassificationModel" title="pyspark.ml.classification.DecisionTreeClassificationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTreeClassificationModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by DecisionTreeClassifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.GBTClassifier.html#pyspark.ml.classification.GBTClassifier" title="pyspark.ml.classification.GBTClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GBTClassifier</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p><a class="reference external" href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a> learning algorithm for classification.It supports binary labels, as well as both continuous and categorical features..</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.GBTClassificationModel.html#pyspark.ml.classification.GBTClassificationModel" title="pyspark.ml.classification.GBTClassificationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GBTClassificationModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by GBTClassifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.RandomForestClassifier.html#pyspark.ml.classification.RandomForestClassifier" title="pyspark.ml.classification.RandomForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a>(*[, featuresCol, …])</p></td>
<td><p><a class="reference external" href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> learning algorithm for classification.It supports both binary and multiclass labels, as well as both continuous and categorical features..</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.RandomForestClassificationModel.html#pyspark.ml.classification.RandomForestClassificationModel" title="pyspark.ml.classification.RandomForestClassificationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestClassificationModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by RandomForestClassifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.RandomForestClassificationSummary.html#pyspark.ml.classification.RandomForestClassificationSummary" title="pyspark.ml.classification.RandomForestClassificationSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestClassificationSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for RandomForestClassification Results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.RandomForestClassificationTrainingSummary.html#pyspark.ml.classification.RandomForestClassificationTrainingSummary" title="pyspark.ml.classification.RandomForestClassificationTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestClassificationTrainingSummary</span></code></a>([…])</p></td>
<td><p>Abstraction for RandomForestClassificationTraining Training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.BinaryRandomForestClassificationSummary.html#pyspark.ml.classification.BinaryRandomForestClassificationSummary" title="pyspark.ml.classification.BinaryRandomForestClassificationSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryRandomForestClassificationSummary</span></code></a>([…])</p></td>
<td><p>BinaryRandomForestClassification results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.BinaryRandomForestClassificationTrainingSummary.html#pyspark.ml.classification.BinaryRandomForestClassificationTrainingSummary" title="pyspark.ml.classification.BinaryRandomForestClassificationTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryRandomForestClassificationTrainingSummary</span></code></a>([…])</p></td>
<td><p>BinaryRandomForestClassification training results for a given model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.NaiveBayes.html#pyspark.ml.classification.NaiveBayes" title="pyspark.ml.classification.NaiveBayes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NaiveBayes</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>Naive Bayes Classifiers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.NaiveBayesModel.html#pyspark.ml.classification.NaiveBayesModel" title="pyspark.ml.classification.NaiveBayesModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NaiveBayesModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by NaiveBayes.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.MultilayerPerceptronClassifier.html#pyspark.ml.classification.MultilayerPerceptronClassifier" title="pyspark.ml.classification.MultilayerPerceptronClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultilayerPerceptronClassifier</span></code></a>(*[, …])</p></td>
<td><p>Classifier trainer based on the Multilayer Perceptron.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.MultilayerPerceptronClassificationModel.html#pyspark.ml.classification.MultilayerPerceptronClassificationModel" title="pyspark.ml.classification.MultilayerPerceptronClassificationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultilayerPerceptronClassificationModel</span></code></a>([…])</p></td>
<td><p>Model fitted by MultilayerPerceptronClassifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.MultilayerPerceptronClassificationSummary.html#pyspark.ml.classification.MultilayerPerceptronClassificationSummary" title="pyspark.ml.classification.MultilayerPerceptronClassificationSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultilayerPerceptronClassificationSummary</span></code></a>([…])</p></td>
<td><p>Abstraction for MultilayerPerceptronClassifier Results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.MultilayerPerceptronClassificationTrainingSummary.html#pyspark.ml.classification.MultilayerPerceptronClassificationTrainingSummary" title="pyspark.ml.classification.MultilayerPerceptronClassificationTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultilayerPerceptronClassificationTrainingSummary</span></code></a>([…])</p></td>
<td><p>Abstraction for MultilayerPerceptronClassifier Training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.OneVsRest.html#pyspark.ml.classification.OneVsRest" title="pyspark.ml.classification.OneVsRest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsRest</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>Reduction of Multiclass Classification to Binary Classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.OneVsRestModel.html#pyspark.ml.classification.OneVsRestModel" title="pyspark.ml.classification.OneVsRestModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsRestModel</span></code></a>(models)</p></td>
<td><p>Model fitted by OneVsRest.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.FMClassifier.html#pyspark.ml.classification.FMClassifier" title="pyspark.ml.classification.FMClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMClassifier</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>Factorization Machines learning algorithm for classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.FMClassificationModel.html#pyspark.ml.classification.FMClassificationModel" title="pyspark.ml.classification.FMClassificationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMClassificationModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.classification.FMClassifier.html#pyspark.ml.classification.FMClassifier" title="pyspark.ml.classification.FMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">FMClassifier</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.classification.FMClassificationSummary.html#pyspark.ml.classification.FMClassificationSummary" title="pyspark.ml.classification.FMClassificationSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMClassificationSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for FMClassifier Results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.classification.FMClassificationTrainingSummary.html#pyspark.ml.classification.FMClassificationTrainingSummary" title="pyspark.ml.classification.FMClassificationTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMClassificationTrainingSummary</span></code></a>([java_obj])</p></td>
<td><p>Abstraction for FMClassifier Training results.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="clustering">
<h2>Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.BisectingKMeans.html#pyspark.ml.clustering.BisectingKMeans" title="pyspark.ml.clustering.BisectingKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BisectingKMeans</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>A bisecting k-means algorithm based on the paper “A comparison of document clustering techniques” by Steinbach, Karypis, and Kumar, with modification to fit Spark.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.BisectingKMeansModel.html#pyspark.ml.clustering.BisectingKMeansModel" title="pyspark.ml.clustering.BisectingKMeansModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BisectingKMeansModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by BisectingKMeans.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.BisectingKMeansSummary.html#pyspark.ml.clustering.BisectingKMeansSummary" title="pyspark.ml.clustering.BisectingKMeansSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BisectingKMeansSummary</span></code></a>([java_obj])</p></td>
<td><p>Bisecting KMeans clustering results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.KMeans.html#pyspark.ml.clustering.KMeans" title="pyspark.ml.clustering.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeans</span></code></a>(*[, featuresCol, predictionCol, k, …])</p></td>
<td><p>K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.KMeansModel.html#pyspark.ml.clustering.KMeansModel" title="pyspark.ml.clustering.KMeansModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeansModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by KMeans.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.KMeansSummary.html#pyspark.ml.clustering.KMeansSummary" title="pyspark.ml.clustering.KMeansSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeansSummary</span></code></a>([java_obj])</p></td>
<td><p>Summary of KMeans.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.GaussianMixture.html#pyspark.ml.clustering.GaussianMixture" title="pyspark.ml.clustering.GaussianMixture"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixture</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>GaussianMixture clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.GaussianMixtureModel.html#pyspark.ml.clustering.GaussianMixtureModel" title="pyspark.ml.clustering.GaussianMixtureModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixtureModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by GaussianMixture.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.GaussianMixtureSummary.html#pyspark.ml.clustering.GaussianMixtureSummary" title="pyspark.ml.clustering.GaussianMixtureSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixtureSummary</span></code></a>([java_obj])</p></td>
<td><p>Gaussian mixture clustering results for a given model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.LDA.html#pyspark.ml.clustering.LDA" title="pyspark.ml.clustering.LDA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LDA</span></code></a>(*[, featuresCol, maxIter, seed, …])</p></td>
<td><p>Latent Dirichlet Allocation (LDA), a topic model designed for text documents.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.LDAModel.html#pyspark.ml.clustering.LDAModel" title="pyspark.ml.clustering.LDAModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LDAModel</span></code></a>([java_model])</p></td>
<td><p>Latent Dirichlet Allocation (LDA) model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.LocalLDAModel.html#pyspark.ml.clustering.LocalLDAModel" title="pyspark.ml.clustering.LocalLDAModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LocalLDAModel</span></code></a>([java_model])</p></td>
<td><p>Local (non-distributed) model fitted by <a class="reference internal" href="api/pyspark.ml.clustering.LDA.html#pyspark.ml.clustering.LDA" title="pyspark.ml.clustering.LDA"><code class="xref py py-class docutils literal notranslate"><span class="pre">LDA</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.DistributedLDAModel.html#pyspark.ml.clustering.DistributedLDAModel" title="pyspark.ml.clustering.DistributedLDAModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistributedLDAModel</span></code></a>([java_model])</p></td>
<td><p>Distributed model fitted by <a class="reference internal" href="api/pyspark.ml.clustering.LDA.html#pyspark.ml.clustering.LDA" title="pyspark.ml.clustering.LDA"><code class="xref py py-class docutils literal notranslate"><span class="pre">LDA</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.clustering.PowerIterationClustering.html#pyspark.ml.clustering.PowerIterationClustering" title="pyspark.ml.clustering.PowerIterationClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PowerIterationClustering</span></code></a>(*[, k, maxIter, …])</p></td>
<td><p>Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by <a class="reference external" href="http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf">Lin and Cohen</a>.From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data..</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.functions.array_to_vector.html#pyspark.ml.functions.array_to_vector" title="pyspark.ml.functions.array_to_vector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_to_vector</span></code></a>(col)</p></td>
<td><p>Converts a column of array of numeric type into a column of pyspark.ml.linalg.DenseVector instances</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.functions.vector_to_array.html#pyspark.ml.functions.vector_to_array" title="pyspark.ml.functions.vector_to_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vector_to_array</span></code></a>(col[, dtype])</p></td>
<td><p>Converts a column of MLlib sparse/dense vectors into a column of dense arrays.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="vector-and-matrix">
<h2>Vector and Matrix<a class="headerlink" href="#vector-and-matrix" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.Vector.html#pyspark.ml.linalg.Vector" title="pyspark.ml.linalg.Vector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Vector</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.DenseVector.html#pyspark.ml.linalg.DenseVector" title="pyspark.ml.linalg.DenseVector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DenseVector</span></code></a>(ar)</p></td>
<td><p>A dense vector represented by a value array.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.SparseVector.html#pyspark.ml.linalg.SparseVector" title="pyspark.ml.linalg.SparseVector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparseVector</span></code></a>(size, *args)</p></td>
<td><p>A simple sparse vector class for passing data to MLlib.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.Vectors.html#pyspark.ml.linalg.Vectors" title="pyspark.ml.linalg.Vectors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Vectors</span></code></a></p></td>
<td><p>Factory methods for working with vectors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.Matrix.html#pyspark.ml.linalg.Matrix" title="pyspark.ml.linalg.Matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Matrix</span></code></a>(numRows, numCols[, isTransposed])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.DenseMatrix.html#pyspark.ml.linalg.DenseMatrix" title="pyspark.ml.linalg.DenseMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DenseMatrix</span></code></a>(numRows, numCols, values[, …])</p></td>
<td><p>Column-major dense matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.SparseMatrix.html#pyspark.ml.linalg.SparseMatrix" title="pyspark.ml.linalg.SparseMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparseMatrix</span></code></a>(numRows, numCols, colPtrs, …)</p></td>
<td><p>Sparse Matrix stored in CSC format.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.linalg.Matrices.html#pyspark.ml.linalg.Matrices" title="pyspark.ml.linalg.Matrices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Matrices</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="recommendation">
<h2>Recommendation<a class="headerlink" href="#recommendation" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS" title="pyspark.ml.recommendation.ALS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ALS</span></code></a>(*[, rank, maxIter, regParam, …])</p></td>
<td><p>Alternating Least Squares (ALS) matrix factorization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.recommendation.ALSModel.html#pyspark.ml.recommendation.ALSModel" title="pyspark.ml.recommendation.ALSModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ALSModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by ALS.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="regression">
<h2>Regression<a class="headerlink" href="#regression" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.AFTSurvivalRegression.html#pyspark.ml.regression.AFTSurvivalRegression" title="pyspark.ml.regression.AFTSurvivalRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AFTSurvivalRegression</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>Accelerated Failure Time (AFT) Model Survival Regression</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.AFTSurvivalRegressionModel.html#pyspark.ml.regression.AFTSurvivalRegressionModel" title="pyspark.ml.regression.AFTSurvivalRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AFTSurvivalRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.AFTSurvivalRegression.html#pyspark.ml.regression.AFTSurvivalRegression" title="pyspark.ml.regression.AFTSurvivalRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">AFTSurvivalRegression</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.DecisionTreeRegressor.html#pyspark.ml.regression.DecisionTreeRegressor" title="pyspark.ml.regression.DecisionTreeRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTreeRegressor</span></code></a>(*[, featuresCol, …])</p></td>
<td><p><p><a class="reference external" href="http://en.wikipedia.org/wiki/Decision_tree_learning">Decision tree</a> learning algorithm for regression.It supports both continuous and categorical features..</p>
</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.DecisionTreeRegressionModel.html#pyspark.ml.regression.DecisionTreeRegressionModel" title="pyspark.ml.regression.DecisionTreeRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTreeRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.DecisionTreeRegressor.html#pyspark.ml.regression.DecisionTreeRegressor" title="pyspark.ml.regression.DecisionTreeRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionTreeRegressor</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GBTRegressor.html#pyspark.ml.regression.GBTRegressor" title="pyspark.ml.regression.GBTRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GBTRegressor</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p><p><a class="reference external" href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a> learning algorithm for regression.It supports both continuous and categorical features..</p>
</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GBTRegressionModel.html#pyspark.ml.regression.GBTRegressionModel" title="pyspark.ml.regression.GBTRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GBTRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.GBTRegressor.html#pyspark.ml.regression.GBTRegressor" title="pyspark.ml.regression.GBTRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GBTRegressor</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GeneralizedLinearRegression.html#pyspark.ml.regression.GeneralizedLinearRegression" title="pyspark.ml.regression.GeneralizedLinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeneralizedLinearRegression</span></code></a>(*[, labelCol, …])</p></td>
<td><p>Generalized Linear Regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GeneralizedLinearRegressionModel.html#pyspark.ml.regression.GeneralizedLinearRegressionModel" title="pyspark.ml.regression.GeneralizedLinearRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeneralizedLinearRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.GeneralizedLinearRegression.html#pyspark.ml.regression.GeneralizedLinearRegression" title="pyspark.ml.regression.GeneralizedLinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">GeneralizedLinearRegression</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GeneralizedLinearRegressionSummary.html#pyspark.ml.regression.GeneralizedLinearRegressionSummary" title="pyspark.ml.regression.GeneralizedLinearRegressionSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeneralizedLinearRegressionSummary</span></code></a>([java_obj])</p></td>
<td><p>Generalized linear regression results evaluated on a dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.GeneralizedLinearRegressionTrainingSummary.html#pyspark.ml.regression.GeneralizedLinearRegressionTrainingSummary" title="pyspark.ml.regression.GeneralizedLinearRegressionTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeneralizedLinearRegressionTrainingSummary</span></code></a>([…])</p></td>
<td><p>Generalized linear regression training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.IsotonicRegression.html#pyspark.ml.regression.IsotonicRegression" title="pyspark.ml.regression.IsotonicRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IsotonicRegression</span></code></a>(*[, featuresCol, …])</p></td>
<td><p>Currently implemented using parallelized pool adjacent violators algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.IsotonicRegressionModel.html#pyspark.ml.regression.IsotonicRegressionModel" title="pyspark.ml.regression.IsotonicRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IsotonicRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.IsotonicRegression.html#pyspark.ml.regression.IsotonicRegression" title="pyspark.ml.regression.IsotonicRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">IsotonicRegression</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.LinearRegression.html#pyspark.ml.regression.LinearRegression" title="pyspark.ml.regression.LinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegression</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>Linear regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.LinearRegressionModel.html#pyspark.ml.regression.LinearRegressionModel" title="pyspark.ml.regression.LinearRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.LinearRegression.html#pyspark.ml.regression.LinearRegression" title="pyspark.ml.regression.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearRegression</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.LinearRegressionSummary.html#pyspark.ml.regression.LinearRegressionSummary" title="pyspark.ml.regression.LinearRegressionSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegressionSummary</span></code></a>([java_obj])</p></td>
<td><p>Linear regression results evaluated on a dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.LinearRegressionTrainingSummary.html#pyspark.ml.regression.LinearRegressionTrainingSummary" title="pyspark.ml.regression.LinearRegressionTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegressionTrainingSummary</span></code></a>([java_obj])</p></td>
<td><p>Linear regression training results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.RandomForestRegressor.html#pyspark.ml.regression.RandomForestRegressor" title="pyspark.ml.regression.RandomForestRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code></a>(*[, featuresCol, …])</p></td>
<td><p><p><a class="reference external" href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> learning algorithm for regression.It supports both continuous and categorical features..</p>
</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.RandomForestRegressionModel.html#pyspark.ml.regression.RandomForestRegressionModel" title="pyspark.ml.regression.RandomForestRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.RandomForestRegressor.html#pyspark.ml.regression.RandomForestRegressor" title="pyspark.ml.regression.RandomForestRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.regression.FMRegressor.html#pyspark.ml.regression.FMRegressor" title="pyspark.ml.regression.FMRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMRegressor</span></code></a>(*[, featuresCol, labelCol, …])</p></td>
<td><p>Factorization Machines learning algorithm for regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.regression.FMRegressionModel.html#pyspark.ml.regression.FMRegressionModel" title="pyspark.ml.regression.FMRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FMRegressionModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by <a class="reference internal" href="api/pyspark.ml.regression.FMRegressor.html#pyspark.ml.regression.FMRegressor" title="pyspark.ml.regression.FMRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">FMRegressor</span></code></a>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="statistics">
<h2>Statistics<a class="headerlink" href="#statistics" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.stat.ChiSquareTest.html#pyspark.ml.stat.ChiSquareTest" title="pyspark.ml.stat.ChiSquareTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSquareTest</span></code></a></p></td>
<td><p>Conduct Pearson’s independence test for every feature against the label.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.stat.Correlation.html#pyspark.ml.stat.Correlation" title="pyspark.ml.stat.Correlation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Correlation</span></code></a></p></td>
<td><p>Compute the correlation matrix for the input dataset of Vectors using the specified method.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.stat.KolmogorovSmirnovTest.html#pyspark.ml.stat.KolmogorovSmirnovTest" title="pyspark.ml.stat.KolmogorovSmirnovTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KolmogorovSmirnovTest</span></code></a></p></td>
<td><p>Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.stat.MultivariateGaussian.html#pyspark.ml.stat.MultivariateGaussian" title="pyspark.ml.stat.MultivariateGaussian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultivariateGaussian</span></code></a>(mean, cov)</p></td>
<td><p>Represents a (mean, cov) tuple</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.stat.Summarizer.html#pyspark.ml.stat.Summarizer" title="pyspark.ml.stat.Summarizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Summarizer</span></code></a></p></td>
<td><p>Tools for vectorized statistics on MLlib Vectors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.stat.SummaryBuilder.html#pyspark.ml.stat.SummaryBuilder" title="pyspark.ml.stat.SummaryBuilder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SummaryBuilder</span></code></a>(jSummaryBuilder)</p></td>
<td><p>A builder object that provides summary statistics about a given column.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="tuning">
<h2>Tuning<a class="headerlink" href="#tuning" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.tuning.ParamGridBuilder.html#pyspark.ml.tuning.ParamGridBuilder" title="pyspark.ml.tuning.ParamGridBuilder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ParamGridBuilder</span></code></a>()</p></td>
<td><p>Builder for a param grid used in grid search-based model selection.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.tuning.CrossValidator.html#pyspark.ml.tuning.CrossValidator" title="pyspark.ml.tuning.CrossValidator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossValidator</span></code></a>(*[, estimator, …])</p></td>
<td><p>K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.tuning.CrossValidatorModel.html#pyspark.ml.tuning.CrossValidatorModel" title="pyspark.ml.tuning.CrossValidatorModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CrossValidatorModel</span></code></a>(bestModel[, avgMetrics, …])</p></td>
<td><p>CrossValidatorModel contains the model with the highest average cross-validation metric across folds and uses this model to transform input data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.tuning.TrainValidationSplit.html#pyspark.ml.tuning.TrainValidationSplit" title="pyspark.ml.tuning.TrainValidationSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TrainValidationSplit</span></code></a>(*[, estimator, …])</p></td>
<td><p>Validation for hyper-parameter tuning.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.tuning.TrainValidationSplitModel.html#pyspark.ml.tuning.TrainValidationSplitModel" title="pyspark.ml.tuning.TrainValidationSplitModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TrainValidationSplitModel</span></code></a>(bestModel[, …])</p></td>
<td><p>Model from train validation split.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="evaluation">
<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.Evaluator.html#pyspark.ml.evaluation.Evaluator" title="pyspark.ml.evaluation.Evaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Evaluator</span></code></a>()</p></td>
<td><p>Base class for evaluators that compute metrics from predictions.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.BinaryClassificationEvaluator.html#pyspark.ml.evaluation.BinaryClassificationEvaluator" title="pyspark.ml.evaluation.BinaryClassificationEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryClassificationEvaluator</span></code></a>(*[, …])</p></td>
<td><p>Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.RegressionEvaluator.html#pyspark.ml.evaluation.RegressionEvaluator" title="pyspark.ml.evaluation.RegressionEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegressionEvaluator</span></code></a>(*[, predictionCol, …])</p></td>
<td><p>Evaluator for Regression, which expects input columns prediction, label and an optional weight column.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.MulticlassClassificationEvaluator.html#pyspark.ml.evaluation.MulticlassClassificationEvaluator" title="pyspark.ml.evaluation.MulticlassClassificationEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MulticlassClassificationEvaluator</span></code></a>(*[, …])</p></td>
<td><p>Evaluator for Multiclass Classification, which expects input columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.MultilabelClassificationEvaluator.html#pyspark.ml.evaluation.MultilabelClassificationEvaluator" title="pyspark.ml.evaluation.MultilabelClassificationEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultilabelClassificationEvaluator</span></code></a>(*[, …])</p></td>
<td><p>Evaluator for Multilabel Classification, which expects two input columns: prediction and label.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.ClusteringEvaluator.html#pyspark.ml.evaluation.ClusteringEvaluator" title="pyspark.ml.evaluation.ClusteringEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ClusteringEvaluator</span></code></a>(*[, predictionCol, …])</p></td>
<td><p>Evaluator for Clustering results, which expects two input columns: prediction and features.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.evaluation.RankingEvaluator.html#pyspark.ml.evaluation.RankingEvaluator" title="pyspark.ml.evaluation.RankingEvaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RankingEvaluator</span></code></a>(*[, predictionCol, …])</p></td>
<td><p>Evaluator for Ranking, which expects two input columns: prediction and label.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="frequency-pattern-mining">
<h2>Frequency Pattern Mining<a class="headerlink" href="#frequency-pattern-mining" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.fpm.FPGrowth.html#pyspark.ml.fpm.FPGrowth" title="pyspark.ml.fpm.FPGrowth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FPGrowth</span></code></a>(*[, minSupport, minConfidence, …])</p></td>
<td><p>A parallel FP-growth algorithm to mine frequent itemsets.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.fpm.FPGrowthModel.html#pyspark.ml.fpm.FPGrowthModel" title="pyspark.ml.fpm.FPGrowthModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FPGrowthModel</span></code></a>([java_model])</p></td>
<td><p>Model fitted by FPGrowth.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.fpm.PrefixSpan.html#pyspark.ml.fpm.PrefixSpan" title="pyspark.ml.fpm.PrefixSpan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrefixSpan</span></code></a>(*[, minSupport, …])</p></td>
<td><p>A parallel PrefixSpan algorithm to mine frequent sequential patterns.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="image">
<h2>Image<a class="headerlink" href="#image" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.image.ImageSchema.html#pyspark.ml.image.ImageSchema" title="pyspark.ml.image.ImageSchema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageSchema</span></code></a></p></td>
<td><p>Internal class for <cite>pyspark.ml.image.ImageSchema</cite> attribute.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.image._ImageSchema.html#pyspark.ml.image._ImageSchema" title="pyspark.ml.image._ImageSchema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_ImageSchema</span></code></a>()</p></td>
<td><p>Internal class for <cite>pyspark.ml.image.ImageSchema</cite> attribute.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="utilities">
<h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this headline"></a></h2>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.BaseReadWrite.html#pyspark.ml.util.BaseReadWrite" title="pyspark.ml.util.BaseReadWrite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseReadWrite</span></code></a>()</p></td>
<td><p>Base class for MLWriter and MLReader.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.DefaultParamsReadable.html#pyspark.ml.util.DefaultParamsReadable" title="pyspark.ml.util.DefaultParamsReadable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DefaultParamsReadable</span></code></a></p></td>
<td><p>Helper trait for making simple <code class="xref py py-class docutils literal notranslate"><span class="pre">Params</span></code> types readable.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.DefaultParamsReader.html#pyspark.ml.util.DefaultParamsReader" title="pyspark.ml.util.DefaultParamsReader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DefaultParamsReader</span></code></a>(cls)</p></td>
<td><p>Specialization of <a class="reference internal" href="api/pyspark.ml.util.MLReader.html#pyspark.ml.util.MLReader" title="pyspark.ml.util.MLReader"><code class="xref py py-class docutils literal notranslate"><span class="pre">MLReader</span></code></a> for <code class="xref py py-class docutils literal notranslate"><span class="pre">Params</span></code> types</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.DefaultParamsWritable.html#pyspark.ml.util.DefaultParamsWritable" title="pyspark.ml.util.DefaultParamsWritable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DefaultParamsWritable</span></code></a></p></td>
<td><p>Helper trait for making simple <code class="xref py py-class docutils literal notranslate"><span class="pre">Params</span></code> types writable.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.DefaultParamsWriter.html#pyspark.ml.util.DefaultParamsWriter" title="pyspark.ml.util.DefaultParamsWriter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DefaultParamsWriter</span></code></a>(instance)</p></td>
<td><p>Specialization of <a class="reference internal" href="api/pyspark.ml.util.MLWriter.html#pyspark.ml.util.MLWriter" title="pyspark.ml.util.MLWriter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MLWriter</span></code></a> for <code class="xref py py-class docutils literal notranslate"><span class="pre">Params</span></code> types</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.GeneralMLWriter.html#pyspark.ml.util.GeneralMLWriter" title="pyspark.ml.util.GeneralMLWriter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeneralMLWriter</span></code></a>()</p></td>
<td><p>Utility class that can save ML instances in different formats.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.HasTrainingSummary.html#pyspark.ml.util.HasTrainingSummary" title="pyspark.ml.util.HasTrainingSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HasTrainingSummary</span></code></a></p></td>
<td><p>Base class for models that provides Training summary.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.Identifiable.html#pyspark.ml.util.Identifiable" title="pyspark.ml.util.Identifiable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Identifiable</span></code></a>()</p></td>
<td><p>Object with a unique ID.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.MLReadable.html#pyspark.ml.util.MLReadable" title="pyspark.ml.util.MLReadable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLReadable</span></code></a></p></td>
<td><p>Mixin for instances that provide <a class="reference internal" href="api/pyspark.ml.util.MLReader.html#pyspark.ml.util.MLReader" title="pyspark.ml.util.MLReader"><code class="xref py py-class docutils literal notranslate"><span class="pre">MLReader</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.MLReader.html#pyspark.ml.util.MLReader" title="pyspark.ml.util.MLReader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLReader</span></code></a>()</p></td>
<td><p>Utility class that can load ML instances.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.ml.util.MLWritable.html#pyspark.ml.util.MLWritable" title="pyspark.ml.util.MLWritable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLWritable</span></code></a></p></td>
<td><p>Mixin for ML instances that provide <a class="reference internal" href="api/pyspark.ml.util.MLWriter.html#pyspark.ml.util.MLWriter" title="pyspark.ml.util.MLWriter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MLWriter</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.ml.util.MLWriter.html#pyspark.ml.util.MLWriter" title="pyspark.ml.util.MLWriter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLWriter</span></code></a>()</p></td>
<td><p>Utility class that can save ML instances.</p></td>
</tr>
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