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| <section id="mllib-rdd-based"> |
| <h1>MLlib (RDD-based)<a class="headerlink" href="#mllib-rdd-based" title="Permalink to this headline">#</a></h1> |
| <section id="classification"> |
| <h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.classification.LogisticRegressionModel.html#pyspark.mllib.classification.LogisticRegressionModel" title="pyspark.mllib.classification.LogisticRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionModel</span></code></a>(weights, intercept, ...)</p></td> |
| <td><p>Classification model trained using Multinomial/Binary Logistic Regression.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.LogisticRegressionWithSGD.html#pyspark.mllib.classification.LogisticRegressionWithSGD" title="pyspark.mllib.classification.LogisticRegressionWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionWithSGD</span></code></a>()</p></td> |
| <td><p>Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.LogisticRegressionWithLBFGS.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS" title="pyspark.mllib.classification.LogisticRegressionWithLBFGS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionWithLBFGS</span></code></a>()</p></td> |
| <td><p>Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.SVMModel.html#pyspark.mllib.classification.SVMModel" title="pyspark.mllib.classification.SVMModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SVMModel</span></code></a>(weights, intercept)</p></td> |
| <td><p>Model for Support Vector Machines (SVMs).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.SVMWithSGD.html#pyspark.mllib.classification.SVMWithSGD" title="pyspark.mllib.classification.SVMWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SVMWithSGD</span></code></a>()</p></td> |
| <td><p>Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.NaiveBayesModel.html#pyspark.mllib.classification.NaiveBayesModel" title="pyspark.mllib.classification.NaiveBayesModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NaiveBayesModel</span></code></a>(labels, pi, theta)</p></td> |
| <td><p>Model for Naive Bayes classifiers.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.NaiveBayes.html#pyspark.mllib.classification.NaiveBayes" title="pyspark.mllib.classification.NaiveBayes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NaiveBayes</span></code></a>()</p></td> |
| <td><p>Train a Multinomial Naive Bayes model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.html#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamingLogisticRegressionWithSGD</span></code></a>([...])</p></td> |
| <td><p>Train or predict a logistic regression model on streaming data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="clustering"> |
| <h2>Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.clustering.BisectingKMeansModel.html#pyspark.mllib.clustering.BisectingKMeansModel" title="pyspark.mllib.clustering.BisectingKMeansModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BisectingKMeansModel</span></code></a>(java_model)</p></td> |
| <td><p>A clustering model derived from the bisecting k-means method.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.BisectingKMeans.html#pyspark.mllib.clustering.BisectingKMeans" title="pyspark.mllib.clustering.BisectingKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BisectingKMeans</span></code></a>()</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-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.KMeansModel.html#pyspark.mllib.clustering.KMeansModel" title="pyspark.mllib.clustering.KMeansModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeansModel</span></code></a>(centers)</p></td> |
| <td><p>A clustering model derived from the k-means method.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.KMeans.html#pyspark.mllib.clustering.KMeans" title="pyspark.mllib.clustering.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeans</span></code></a>()</p></td> |
| <td><p>K-means clustering.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.GaussianMixtureModel.html#pyspark.mllib.clustering.GaussianMixtureModel" title="pyspark.mllib.clustering.GaussianMixtureModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixtureModel</span></code></a>(java_model)</p></td> |
| <td><p>A clustering model derived from the Gaussian Mixture Model method.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.GaussianMixture.html#pyspark.mllib.clustering.GaussianMixture" title="pyspark.mllib.clustering.GaussianMixture"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixture</span></code></a>()</p></td> |
| <td><p>Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.PowerIterationClusteringModel.html#pyspark.mllib.clustering.PowerIterationClusteringModel" title="pyspark.mllib.clustering.PowerIterationClusteringModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PowerIterationClusteringModel</span></code></a>(java_model)</p></td> |
| <td><p>Model produced by <a class="reference internal" href="api/pyspark.mllib.clustering.PowerIterationClustering.html#pyspark.mllib.clustering.PowerIterationClustering" title="pyspark.mllib.clustering.PowerIterationClustering"><code class="xref py py-class docutils literal notranslate"><span class="pre">PowerIterationClustering</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.PowerIterationClustering.html#pyspark.mllib.clustering.PowerIterationClustering" title="pyspark.mllib.clustering.PowerIterationClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PowerIterationClustering</span></code></a>()</p></td> |
| <td><p>Power Iteration Clustering (PIC), a scalable graph clustering algorithm.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.StreamingKMeans.html#pyspark.mllib.clustering.StreamingKMeans" title="pyspark.mllib.clustering.StreamingKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamingKMeans</span></code></a>([k, decayFactor, timeUnit])</p></td> |
| <td><p>Provides methods to set k, decayFactor, timeUnit to configure the KMeans algorithm for fitting and predicting on incoming dstreams.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.StreamingKMeansModel.html#pyspark.mllib.clustering.StreamingKMeansModel" title="pyspark.mllib.clustering.StreamingKMeansModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamingKMeansModel</span></code></a>(clusterCenters, ...)</p></td> |
| <td><p>Clustering model which can perform an online update of the centroids.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.LDA.html#pyspark.mllib.clustering.LDA" title="pyspark.mllib.clustering.LDA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LDA</span></code></a>()</p></td> |
| <td><p>Train Latent Dirichlet Allocation (LDA) model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.clustering.LDAModel.html#pyspark.mllib.clustering.LDAModel" title="pyspark.mllib.clustering.LDAModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LDAModel</span></code></a>(java_model)</p></td> |
| <td><p>A clustering model derived from the LDA method.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="evaluation"> |
| <h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.evaluation.BinaryClassificationMetrics.html#pyspark.mllib.evaluation.BinaryClassificationMetrics" title="pyspark.mllib.evaluation.BinaryClassificationMetrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BinaryClassificationMetrics</span></code></a>(scoreAndLabels)</p></td> |
| <td><p>Evaluator for binary classification.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.evaluation.RegressionMetrics.html#pyspark.mllib.evaluation.RegressionMetrics" title="pyspark.mllib.evaluation.RegressionMetrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegressionMetrics</span></code></a>(predictionAndObservations)</p></td> |
| <td><p>Evaluator for regression.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.evaluation.MulticlassMetrics.html#pyspark.mllib.evaluation.MulticlassMetrics" title="pyspark.mllib.evaluation.MulticlassMetrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MulticlassMetrics</span></code></a>(predictionAndLabels)</p></td> |
| <td><p>Evaluator for multiclass classification.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.evaluation.RankingMetrics.html#pyspark.mllib.evaluation.RankingMetrics" title="pyspark.mllib.evaluation.RankingMetrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RankingMetrics</span></code></a>(predictionAndLabels)</p></td> |
| <td><p>Evaluator for ranking algorithms.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="feature"> |
| <h2>Feature<a class="headerlink" href="#feature" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.feature.Normalizer.html#pyspark.mllib.feature.Normalizer" title="pyspark.mllib.feature.Normalizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Normalizer</span></code></a>([p])</p></td> |
| <td><p>Normalizes samples individually to unit L<sup>p</sup> norm</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.StandardScalerModel.html#pyspark.mllib.feature.StandardScalerModel" title="pyspark.mllib.feature.StandardScalerModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StandardScalerModel</span></code></a>(java_model)</p></td> |
| <td><p>Represents a StandardScaler model that can transform vectors.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.StandardScaler.html#pyspark.mllib.feature.StandardScaler" title="pyspark.mllib.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.mllib.feature.HashingTF.html#pyspark.mllib.feature.HashingTF" title="pyspark.mllib.feature.HashingTF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HashingTF</span></code></a>([numFeatures])</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.mllib.feature.IDFModel.html#pyspark.mllib.feature.IDFModel" title="pyspark.mllib.feature.IDFModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IDFModel</span></code></a>(java_model)</p></td> |
| <td><p>Represents an IDF model that can transform term frequency vectors.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.IDF.html#pyspark.mllib.feature.IDF" title="pyspark.mllib.feature.IDF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IDF</span></code></a>([minDocFreq])</p></td> |
| <td><p>Inverse document frequency (IDF).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.Word2Vec.html#pyspark.mllib.feature.Word2Vec" title="pyspark.mllib.feature.Word2Vec"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Word2Vec</span></code></a>()</p></td> |
| <td><p>Word2Vec creates vector representation of words in a text corpus.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.Word2VecModel.html#pyspark.mllib.feature.Word2VecModel" title="pyspark.mllib.feature.Word2VecModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Word2VecModel</span></code></a>(java_model)</p></td> |
| <td><p>class for Word2Vec model</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.ChiSqSelector.html#pyspark.mllib.feature.ChiSqSelector" title="pyspark.mllib.feature.ChiSqSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqSelector</span></code></a>([numTopFeatures, ...])</p></td> |
| <td><p>Creates a ChiSquared feature selector.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.ChiSqSelectorModel.html#pyspark.mllib.feature.ChiSqSelectorModel" title="pyspark.mllib.feature.ChiSqSelectorModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqSelectorModel</span></code></a>(java_model)</p></td> |
| <td><p>Represents a Chi Squared selector model.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.feature.ElementwiseProduct.html#pyspark.mllib.feature.ElementwiseProduct" title="pyspark.mllib.feature.ElementwiseProduct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElementwiseProduct</span></code></a>(scalingVector)</p></td> |
| <td><p>Scales each column of the vector, with the supplied weight vector.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <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="autosummary 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.mllib.fpm.FPGrowth.html#pyspark.mllib.fpm.FPGrowth" title="pyspark.mllib.fpm.FPGrowth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FPGrowth</span></code></a>()</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.mllib.fpm.FPGrowthModel.html#pyspark.mllib.fpm.FPGrowthModel" title="pyspark.mllib.fpm.FPGrowthModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FPGrowthModel</span></code></a>(java_model)</p></td> |
| <td><p>A FP-Growth model for mining frequent itemsets using the Parallel FP-Growth algorithm.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.fpm.PrefixSpan.html#pyspark.mllib.fpm.PrefixSpan" title="pyspark.mllib.fpm.PrefixSpan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrefixSpan</span></code></a>()</p></td> |
| <td><p>A parallel PrefixSpan algorithm to mine frequent sequential patterns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.fpm.PrefixSpanModel.html#pyspark.mllib.fpm.PrefixSpanModel" title="pyspark.mllib.fpm.PrefixSpanModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrefixSpanModel</span></code></a>(java_model)</p></td> |
| <td><p>Model fitted by PrefixSpan</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <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="autosummary 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.mllib.linalg.Vector.html#pyspark.mllib.linalg.Vector" title="pyspark.mllib.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.mllib.linalg.DenseVector.html#pyspark.mllib.linalg.DenseVector" title="pyspark.mllib.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.mllib.linalg.SparseVector.html#pyspark.mllib.linalg.SparseVector" title="pyspark.mllib.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.mllib.linalg.Vectors.html#pyspark.mllib.linalg.Vectors" title="pyspark.mllib.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.mllib.linalg.Matrix.html#pyspark.mllib.linalg.Matrix" title="pyspark.mllib.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.mllib.linalg.DenseMatrix.html#pyspark.mllib.linalg.DenseMatrix" title="pyspark.mllib.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.mllib.linalg.SparseMatrix.html#pyspark.mllib.linalg.SparseMatrix" title="pyspark.mllib.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.mllib.linalg.Matrices.html#pyspark.mllib.linalg.Matrices" title="pyspark.mllib.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> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.QRDecomposition.html#pyspark.mllib.linalg.QRDecomposition" title="pyspark.mllib.linalg.QRDecomposition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QRDecomposition</span></code></a>(Q, R)</p></td> |
| <td><p>Represents QR factors.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <section id="distributed-representation"> |
| <h3>Distributed Representation<a class="headerlink" href="#distributed-representation" title="Permalink to this headline">#</a></h3> |
| <table class="autosummary 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.mllib.linalg.distributed.BlockMatrix.html#pyspark.mllib.linalg.distributed.BlockMatrix" title="pyspark.mllib.linalg.distributed.BlockMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BlockMatrix</span></code></a>(blocks, rowsPerBlock, colsPerBlock)</p></td> |
| <td><p>Represents a distributed matrix in blocks of local matrices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.CoordinateMatrix.html#pyspark.mllib.linalg.distributed.CoordinateMatrix" title="pyspark.mllib.linalg.distributed.CoordinateMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CoordinateMatrix</span></code></a>(entries[, numRows, numCols])</p></td> |
| <td><p>Represents a matrix in coordinate format.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.DistributedMatrix.html#pyspark.mllib.linalg.distributed.DistributedMatrix" title="pyspark.mllib.linalg.distributed.DistributedMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistributedMatrix</span></code></a>()</p></td> |
| <td><p>Represents a distributively stored matrix backed by one or more RDDs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.IndexedRow.html#pyspark.mllib.linalg.distributed.IndexedRow" title="pyspark.mllib.linalg.distributed.IndexedRow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IndexedRow</span></code></a>(index, vector)</p></td> |
| <td><p>Represents a row of an IndexedRowMatrix.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.IndexedRowMatrix.html#pyspark.mllib.linalg.distributed.IndexedRowMatrix" title="pyspark.mllib.linalg.distributed.IndexedRowMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IndexedRowMatrix</span></code></a>(rows[, numRows, numCols])</p></td> |
| <td><p>Represents a row-oriented distributed Matrix with indexed rows.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.MatrixEntry.html#pyspark.mllib.linalg.distributed.MatrixEntry" title="pyspark.mllib.linalg.distributed.MatrixEntry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MatrixEntry</span></code></a>(i, j, value)</p></td> |
| <td><p>Represents an entry of a CoordinateMatrix.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.RowMatrix.html#pyspark.mllib.linalg.distributed.RowMatrix" title="pyspark.mllib.linalg.distributed.RowMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RowMatrix</span></code></a>(rows[, numRows, numCols])</p></td> |
| <td><p>Represents a row-oriented distributed Matrix with no meaningful row indices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.linalg.distributed.SingularValueDecomposition.html#pyspark.mllib.linalg.distributed.SingularValueDecomposition" title="pyspark.mllib.linalg.distributed.SingularValueDecomposition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SingularValueDecomposition</span></code></a>(java_model)</p></td> |
| <td><p>Represents singular value decomposition (SVD) factors.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| </section> |
| <section id="random"> |
| <h2>Random<a class="headerlink" href="#random" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs" title="pyspark.mllib.random.RandomRDDs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomRDDs</span></code></a>()</p></td> |
| <td><p>Generator methods for creating RDDs comprised of i.i.d samples from some distribution.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="recommendation"> |
| <h2>Recommendation<a class="headerlink" href="#recommendation" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.recommendation.MatrixFactorizationModel.html#pyspark.mllib.recommendation.MatrixFactorizationModel" title="pyspark.mllib.recommendation.MatrixFactorizationModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MatrixFactorizationModel</span></code></a>(java_model)</p></td> |
| <td><p>A matrix factorisation model trained by regularized alternating least-squares.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.recommendation.ALS.html#pyspark.mllib.recommendation.ALS" title="pyspark.mllib.recommendation.ALS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ALS</span></code></a>()</p></td> |
| <td><p>Alternating Least Squares matrix factorization</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.recommendation.Rating.html#pyspark.mllib.recommendation.Rating" title="pyspark.mllib.recommendation.Rating"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rating</span></code></a>(user, product, rating)</p></td> |
| <td><p>Represents a (user, product, rating) tuple.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="regression"> |
| <h2>Regression<a class="headerlink" href="#regression" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.regression.LabeledPoint.html#pyspark.mllib.regression.LabeledPoint" title="pyspark.mllib.regression.LabeledPoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LabeledPoint</span></code></a>(label, features)</p></td> |
| <td><p>Class that represents the features and labels of a data point.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.LinearModel.html#pyspark.mllib.regression.LinearModel" title="pyspark.mllib.regression.LinearModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearModel</span></code></a>(weights, intercept)</p></td> |
| <td><p>A linear model that has a vector of coefficients and an intercept.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.LinearRegressionModel.html#pyspark.mllib.regression.LinearRegressionModel" title="pyspark.mllib.regression.LinearRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegressionModel</span></code></a>(weights, intercept)</p></td> |
| <td><p>A linear regression model derived from a least-squares fit.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.LinearRegressionWithSGD.html#pyspark.mllib.regression.LinearRegressionWithSGD" title="pyspark.mllib.regression.LinearRegressionWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegressionWithSGD</span></code></a>()</p></td> |
| <td><p>Train a linear regression model with no regularization using Stochastic Gradient Descent.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.RidgeRegressionModel.html#pyspark.mllib.regression.RidgeRegressionModel" title="pyspark.mllib.regression.RidgeRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeRegressionModel</span></code></a>(weights, intercept)</p></td> |
| <td><p>A linear regression model derived from a least-squares fit with an l_2 penalty term.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.RidgeRegressionWithSGD.html#pyspark.mllib.regression.RidgeRegressionWithSGD" title="pyspark.mllib.regression.RidgeRegressionWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeRegressionWithSGD</span></code></a>()</p></td> |
| <td><p>Train a regression model with L2-regularization using Stochastic Gradient Descent.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.LassoModel.html#pyspark.mllib.regression.LassoModel" title="pyspark.mllib.regression.LassoModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoModel</span></code></a>(weights, intercept)</p></td> |
| <td><p>A linear regression model derived from a least-squares fit with an l_1 penalty term.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.LassoWithSGD.html#pyspark.mllib.regression.LassoWithSGD" title="pyspark.mllib.regression.LassoWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoWithSGD</span></code></a>()</p></td> |
| <td><p>Train a regression model with L1-regularization using Stochastic Gradient Descent.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.IsotonicRegressionModel.html#pyspark.mllib.regression.IsotonicRegressionModel" title="pyspark.mllib.regression.IsotonicRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IsotonicRegressionModel</span></code></a>(boundaries, ...)</p></td> |
| <td><p>Regression model for isotonic regression.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.IsotonicRegression.html#pyspark.mllib.regression.IsotonicRegression" title="pyspark.mllib.regression.IsotonicRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IsotonicRegression</span></code></a>()</p></td> |
| <td><p>Isotonic regression.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.StreamingLinearAlgorithm.html#pyspark.mllib.regression.StreamingLinearAlgorithm" title="pyspark.mllib.regression.StreamingLinearAlgorithm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamingLinearAlgorithm</span></code></a>(model)</p></td> |
| <td><p>Base class that has to be inherited by any StreamingLinearAlgorithm.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.regression.StreamingLinearRegressionWithSGD.html#pyspark.mllib.regression.StreamingLinearRegressionWithSGD" title="pyspark.mllib.regression.StreamingLinearRegressionWithSGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamingLinearRegressionWithSGD</span></code></a>([stepSize, ...])</p></td> |
| <td><p>Train or predict a linear regression model on streaming data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="statistics"> |
| <h2>Statistics<a class="headerlink" href="#statistics" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.stat.Statistics.html#pyspark.mllib.stat.Statistics" title="pyspark.mllib.stat.Statistics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Statistics</span></code></a>()</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.MultivariateStatisticalSummary.html#pyspark.mllib.stat.MultivariateStatisticalSummary" title="pyspark.mllib.stat.MultivariateStatisticalSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultivariateStatisticalSummary</span></code></a>(java_model)</p></td> |
| <td><p>Trait for multivariate statistical summary of a data matrix.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.ChiSqTestResult.html#pyspark.mllib.stat.ChiSqTestResult" title="pyspark.mllib.stat.ChiSqTestResult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqTestResult</span></code></a>(java_model)</p></td> |
| <td><p>Contains test results for the chi-squared hypothesis test.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.MultivariateGaussian.html#pyspark.mllib.stat.MultivariateGaussian" title="pyspark.mllib.stat.MultivariateGaussian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultivariateGaussian</span></code></a>(mu, sigma)</p></td> |
| <td><p>Represents a (mu, sigma) tuple</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.KernelDensity.html#pyspark.mllib.stat.KernelDensity" title="pyspark.mllib.stat.KernelDensity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KernelDensity</span></code></a>()</p></td> |
| <td><p>Estimate probability density at required points given an RDD of samples from the population.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.ChiSqTestResult.html#pyspark.mllib.stat.ChiSqTestResult" title="pyspark.mllib.stat.ChiSqTestResult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChiSqTestResult</span></code></a>(java_model)</p></td> |
| <td><p>Contains test results for the chi-squared hypothesis test.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.stat.KolmogorovSmirnovTestResult.html#pyspark.mllib.stat.KolmogorovSmirnovTestResult" title="pyspark.mllib.stat.KolmogorovSmirnovTestResult"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KolmogorovSmirnovTestResult</span></code></a>(java_model)</p></td> |
| <td><p>Contains test results for the Kolmogorov-Smirnov test.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="tree"> |
| <h2>Tree<a class="headerlink" href="#tree" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel" title="pyspark.mllib.tree.DecisionTreeModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTreeModel</span></code></a>(java_model)</p></td> |
| <td><p>A decision tree model for classification or regression.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.tree.DecisionTree.html#pyspark.mllib.tree.DecisionTree" title="pyspark.mllib.tree.DecisionTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecisionTree</span></code></a>()</p></td> |
| <td><p>Learning algorithm for a decision tree model for classification or regression.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.tree.RandomForestModel.html#pyspark.mllib.tree.RandomForestModel" title="pyspark.mllib.tree.RandomForestModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestModel</span></code></a>(java_model)</p></td> |
| <td><p>Represents a random forest model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.tree.RandomForest.html#pyspark.mllib.tree.RandomForest" title="pyspark.mllib.tree.RandomForest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForest</span></code></a>()</p></td> |
| <td><p>Learning algorithm for a random forest model for classification or regression.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.tree.GradientBoostedTreesModel.html#pyspark.mllib.tree.GradientBoostedTreesModel" title="pyspark.mllib.tree.GradientBoostedTreesModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GradientBoostedTreesModel</span></code></a>(java_model)</p></td> |
| <td><p>Represents a gradient-boosted tree model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.tree.GradientBoostedTrees.html#pyspark.mllib.tree.GradientBoostedTrees" title="pyspark.mllib.tree.GradientBoostedTrees"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GradientBoostedTrees</span></code></a>()</p></td> |
| <td><p>Learning algorithm for a gradient boosted trees model for classification or regression.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="utilities"> |
| <h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this headline">#</a></h2> |
| <table class="autosummary 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.mllib.util.JavaLoader.html#pyspark.mllib.util.JavaLoader" title="pyspark.mllib.util.JavaLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JavaLoader</span></code></a>()</p></td> |
| <td><p>Mixin for classes which can load saved models using its Scala implementation.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.util.JavaSaveable.html#pyspark.mllib.util.JavaSaveable" title="pyspark.mllib.util.JavaSaveable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JavaSaveable</span></code></a>()</p></td> |
| <td><p>Mixin for models that provide save() through their Scala implementation.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.util.LinearDataGenerator.html#pyspark.mllib.util.LinearDataGenerator" title="pyspark.mllib.util.LinearDataGenerator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearDataGenerator</span></code></a>()</p></td> |
| <td><p>Utils for generating linear data.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.util.Loader.html#pyspark.mllib.util.Loader" title="pyspark.mllib.util.Loader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Loader</span></code></a>()</p></td> |
| <td><p>Mixin for classes which can load saved models from files.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="api/pyspark.mllib.util.MLUtils.html#pyspark.mllib.util.MLUtils" title="pyspark.mllib.util.MLUtils"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLUtils</span></code></a>()</p></td> |
| <td><p>Helper methods to load, save and pre-process data used in MLlib.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="api/pyspark.mllib.util.Saveable.html#pyspark.mllib.util.Saveable" title="pyspark.mllib.util.Saveable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Saveable</span></code></a>()</p></td> |
| <td><p>Mixin for models and transformers which may be saved as files.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| </section> |
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