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| <div class="section" id="linearsvcmodel"> |
| <h1>LinearSVCModel<a class="headerlink" href="#linearsvcmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.ml.classification.LinearSVCModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.ml.classification.</code><code class="sig-name descname">LinearSVCModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">java_model</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>JavaObject<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/ml/classification.html#LinearSVCModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model fitted by LinearSVC.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.2.0.</span></p> |
| </div> |
| <p class="rubric">Methods</p> |
| <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="#pyspark.ml.classification.LinearSVCModel.clear" title="pyspark.ml.classification.LinearSVCModel.clear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code></a>(param)</p></td> |
| <td><p>Clears a param from the param map if it has been explicitly set.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.copy" title="pyspark.ml.classification.LinearSVCModel.copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a>([extra])</p></td> |
| <td><p>Creates a copy of this instance with the same uid and some extra params.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.evaluate" title="pyspark.ml.classification.LinearSVCModel.evaluate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate</span></code></a>(dataset)</p></td> |
| <td><p>Evaluates the model on a test dataset.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.explainParam" title="pyspark.ml.classification.LinearSVCModel.explainParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explainParam</span></code></a>(param)</p></td> |
| <td><p>Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.explainParams" title="pyspark.ml.classification.LinearSVCModel.explainParams"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explainParams</span></code></a>()</p></td> |
| <td><p>Returns the documentation of all params with their optionally default values and user-supplied values.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.extractParamMap" title="pyspark.ml.classification.LinearSVCModel.extractParamMap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extractParamMap</span></code></a>([extra])</p></td> |
| <td><p>Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getAggregationDepth" title="pyspark.ml.classification.LinearSVCModel.getAggregationDepth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getAggregationDepth</span></code></a>()</p></td> |
| <td><p>Gets the value of aggregationDepth or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getFeaturesCol" title="pyspark.ml.classification.LinearSVCModel.getFeaturesCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getFeaturesCol</span></code></a>()</p></td> |
| <td><p>Gets the value of featuresCol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getFitIntercept" title="pyspark.ml.classification.LinearSVCModel.getFitIntercept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getFitIntercept</span></code></a>()</p></td> |
| <td><p>Gets the value of fitIntercept or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getLabelCol" title="pyspark.ml.classification.LinearSVCModel.getLabelCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getLabelCol</span></code></a>()</p></td> |
| <td><p>Gets the value of labelCol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getMaxBlockSizeInMB" title="pyspark.ml.classification.LinearSVCModel.getMaxBlockSizeInMB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getMaxBlockSizeInMB</span></code></a>()</p></td> |
| <td><p>Gets the value of maxBlockSizeInMB or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getMaxIter" title="pyspark.ml.classification.LinearSVCModel.getMaxIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getMaxIter</span></code></a>()</p></td> |
| <td><p>Gets the value of maxIter or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getOrDefault" title="pyspark.ml.classification.LinearSVCModel.getOrDefault"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getOrDefault</span></code></a>(param)</p></td> |
| <td><p>Gets the value of a param in the user-supplied param map or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getParam" title="pyspark.ml.classification.LinearSVCModel.getParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getParam</span></code></a>(paramName)</p></td> |
| <td><p>Gets a param by its name.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getPredictionCol" title="pyspark.ml.classification.LinearSVCModel.getPredictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getPredictionCol</span></code></a>()</p></td> |
| <td><p>Gets the value of predictionCol or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getRawPredictionCol" title="pyspark.ml.classification.LinearSVCModel.getRawPredictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getRawPredictionCol</span></code></a>()</p></td> |
| <td><p>Gets the value of rawPredictionCol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getRegParam" title="pyspark.ml.classification.LinearSVCModel.getRegParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getRegParam</span></code></a>()</p></td> |
| <td><p>Gets the value of regParam or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getStandardization" title="pyspark.ml.classification.LinearSVCModel.getStandardization"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getStandardization</span></code></a>()</p></td> |
| <td><p>Gets the value of standardization or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getThreshold" title="pyspark.ml.classification.LinearSVCModel.getThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getThreshold</span></code></a>()</p></td> |
| <td><p>Gets the value of threshold or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getTol" title="pyspark.ml.classification.LinearSVCModel.getTol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getTol</span></code></a>()</p></td> |
| <td><p>Gets the value of tol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.getWeightCol" title="pyspark.ml.classification.LinearSVCModel.getWeightCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getWeightCol</span></code></a>()</p></td> |
| <td><p>Gets the value of weightCol or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.hasDefault" title="pyspark.ml.classification.LinearSVCModel.hasDefault"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hasDefault</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param has a default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.hasParam" title="pyspark.ml.classification.LinearSVCModel.hasParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hasParam</span></code></a>(paramName)</p></td> |
| <td><p>Tests whether this instance contains a param with a given (string) name.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.isDefined" title="pyspark.ml.classification.LinearSVCModel.isDefined"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isDefined</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param is explicitly set by user or has a default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.isSet" title="pyspark.ml.classification.LinearSVCModel.isSet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isSet</span></code></a>(param)</p></td> |
| <td><p>Checks whether a param is explicitly set by user.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.load" title="pyspark.ml.classification.LinearSVCModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td> |
| <td><p>Reads an ML instance from the input path, a shortcut of <cite>read().load(path)</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.predict" title="pyspark.ml.classification.LinearSVCModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(value)</p></td> |
| <td><p>Predict label for the given features.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.predictRaw" title="pyspark.ml.classification.LinearSVCModel.predictRaw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictRaw</span></code></a>(value)</p></td> |
| <td><p>Raw prediction for each possible label.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.read" title="pyspark.ml.classification.LinearSVCModel.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>()</p></td> |
| <td><p>Returns an MLReader instance for this class.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.save" title="pyspark.ml.classification.LinearSVCModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(path)</p></td> |
| <td><p>Save this ML instance to the given path, a shortcut of ‘write().save(path)’.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.set" title="pyspark.ml.classification.LinearSVCModel.set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set</span></code></a>(param, value)</p></td> |
| <td><p>Sets a parameter in the embedded param map.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.setFeaturesCol" title="pyspark.ml.classification.LinearSVCModel.setFeaturesCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setFeaturesCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.featuresCol" title="pyspark.ml.classification.LinearSVCModel.featuresCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">featuresCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.setPredictionCol" title="pyspark.ml.classification.LinearSVCModel.setPredictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setPredictionCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.predictionCol" title="pyspark.ml.classification.LinearSVCModel.predictionCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">predictionCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.setRawPredictionCol" title="pyspark.ml.classification.LinearSVCModel.setRawPredictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setRawPredictionCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.rawPredictionCol" title="pyspark.ml.classification.LinearSVCModel.rawPredictionCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">rawPredictionCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.setThreshold" title="pyspark.ml.classification.LinearSVCModel.setThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setThreshold</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.threshold" title="pyspark.ml.classification.LinearSVCModel.threshold"><code class="xref py py-attr docutils literal notranslate"><span class="pre">threshold</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.summary" title="pyspark.ml.classification.LinearSVCModel.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>()</p></td> |
| <td><p>Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.transform" title="pyspark.ml.classification.LinearSVCModel.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(dataset[, params])</p></td> |
| <td><p>Transforms the input dataset with optional parameters.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.write" title="pyspark.ml.classification.LinearSVCModel.write"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write</span></code></a>()</p></td> |
| <td><p>Returns an MLWriter instance for this ML instance.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Attributes</p> |
| <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="#pyspark.ml.classification.LinearSVCModel.aggregationDepth" title="pyspark.ml.classification.LinearSVCModel.aggregationDepth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">aggregationDepth</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.coefficients" title="pyspark.ml.classification.LinearSVCModel.coefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coefficients</span></code></a></p></td> |
| <td><p>Model coefficients of Linear SVM Classifier.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.featuresCol" title="pyspark.ml.classification.LinearSVCModel.featuresCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">featuresCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.fitIntercept" title="pyspark.ml.classification.LinearSVCModel.fitIntercept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fitIntercept</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.hasSummary" title="pyspark.ml.classification.LinearSVCModel.hasSummary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hasSummary</span></code></a></p></td> |
| <td><p>Indicates whether a training summary exists for this model instance.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.intercept" title="pyspark.ml.classification.LinearSVCModel.intercept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intercept</span></code></a></p></td> |
| <td><p>Model intercept of Linear SVM Classifier.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.labelCol" title="pyspark.ml.classification.LinearSVCModel.labelCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">labelCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.maxBlockSizeInMB" title="pyspark.ml.classification.LinearSVCModel.maxBlockSizeInMB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maxBlockSizeInMB</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.maxIter" title="pyspark.ml.classification.LinearSVCModel.maxIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maxIter</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.numClasses" title="pyspark.ml.classification.LinearSVCModel.numClasses"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numClasses</span></code></a></p></td> |
| <td><p>Number of classes (values which the label can take).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.numFeatures" title="pyspark.ml.classification.LinearSVCModel.numFeatures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numFeatures</span></code></a></p></td> |
| <td><p>Returns the number of features the model was trained on.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.params" title="pyspark.ml.classification.LinearSVCModel.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td> |
| <td><p>Returns all params ordered by name.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.predictionCol" title="pyspark.ml.classification.LinearSVCModel.predictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictionCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.rawPredictionCol" title="pyspark.ml.classification.LinearSVCModel.rawPredictionCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rawPredictionCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.regParam" title="pyspark.ml.classification.LinearSVCModel.regParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">regParam</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.standardization" title="pyspark.ml.classification.LinearSVCModel.standardization"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standardization</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.threshold" title="pyspark.ml.classification.LinearSVCModel.threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.tol" title="pyspark.ml.classification.LinearSVCModel.tol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.weightCol" title="pyspark.ml.classification.LinearSVCModel.weightCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">weightCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.clear"> |
| <code class="sig-name descname">clear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a></span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.clear" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Clears a param from the param map if it has been explicitly set.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.copy"> |
| <code class="sig-name descname">copy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → JP<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.copy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Creates a copy of this instance with the same uid and some |
| extra params. This implementation first calls Params.copy and |
| then make a copy of the companion Java pipeline component with |
| extra params. So both the Python wrapper and the Java pipeline |
| component get copied.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>extra</strong><span class="classifier">dict, optional</span></dt><dd><p>Extra parameters to copy to the new instance</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><code class="xref py py-class docutils literal notranslate"><span class="pre">JavaParams</span></code></dt><dd><p>Copy of this instance</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.evaluate"> |
| <code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.ml.classification.LinearSVCSummary.html#pyspark.ml.classification.LinearSVCSummary" title="pyspark.ml.classification.LinearSVCSummary">pyspark.ml.classification.LinearSVCSummary</a><a class="reference internal" href="../../_modules/pyspark/ml/classification.html#LinearSVCModel.evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.evaluate" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Evaluates the model on a test dataset.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.1.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>dataset</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>Test dataset to evaluate model on.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.explainParam"> |
| <code class="sig-name descname">explainParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">]</span></span></em><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.explainParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Explains a single param and returns its name, doc, and optional |
| default value and user-supplied value in a string.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.explainParams"> |
| <code class="sig-name descname">explainParams</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.explainParams" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the documentation of all params with their optionally |
| default values and user-supplied values.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.extractParamMap"> |
| <code class="sig-name descname">extractParamMap</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → ParamMap<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.extractParamMap" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Extracts the embedded default param values and user-supplied |
| values, and then merges them with extra values from input into |
| a flat param map, where the latter value is used if there exist |
| conflicts, i.e., with ordering: default param values < |
| user-supplied values < extra.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>extra</strong><span class="classifier">dict, optional</span></dt><dd><p>extra param values</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>dict</dt><dd><p>merged param map</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getAggregationDepth"> |
| <code class="sig-name descname">getAggregationDepth</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getAggregationDepth" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of aggregationDepth or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getFeaturesCol"> |
| <code class="sig-name descname">getFeaturesCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getFeaturesCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of featuresCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getFitIntercept"> |
| <code class="sig-name descname">getFitIntercept</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getFitIntercept" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of fitIntercept or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getLabelCol"> |
| <code class="sig-name descname">getLabelCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getLabelCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of labelCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getMaxBlockSizeInMB"> |
| <code class="sig-name descname">getMaxBlockSizeInMB</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getMaxBlockSizeInMB" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of maxBlockSizeInMB or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getMaxIter"> |
| <code class="sig-name descname">getMaxIter</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getMaxIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of maxIter or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getOrDefault"> |
| <code class="sig-name descname">getOrDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>T<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → Union<span class="p">[</span>Any<span class="p">, </span>T<span class="p">]</span><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getOrDefault" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of a param in the user-supplied param map or its |
| default value. Raises an error if neither is set.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getParam"> |
| <code class="sig-name descname">getParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets a param by its name.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getPredictionCol"> |
| <code class="sig-name descname">getPredictionCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of predictionCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getRawPredictionCol"> |
| <code class="sig-name descname">getRawPredictionCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getRawPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of rawPredictionCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getRegParam"> |
| <code class="sig-name descname">getRegParam</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getRegParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of regParam or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getStandardization"> |
| <code class="sig-name descname">getStandardization</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getStandardization" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of standardization or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getThreshold"> |
| <code class="sig-name descname">getThreshold</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of threshold or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getTol"> |
| <code class="sig-name descname">getTol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getTol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of tol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.getWeightCol"> |
| <code class="sig-name descname">getWeightCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → str<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.getWeightCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of weightCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.hasDefault"> |
| <code class="sig-name descname">hasDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.hasDefault" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param has a default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.hasParam"> |
| <code class="sig-name descname">hasParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.hasParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Tests whether this instance contains a param with a given |
| (string) name.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.isDefined"> |
| <code class="sig-name descname">isDefined</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.isDefined" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param is explicitly set by user or has |
| a default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.isSet"> |
| <code class="sig-name descname">isSet</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a><span class="p">[</span>Any<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.isSet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether a param is explicitly set by user.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.load"> |
| <em class="property">classmethod </em><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → RL<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reads an ML instance from the input path, a shortcut of <cite>read().load(path)</cite>.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.predict"> |
| <code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">T</span></em><span class="sig-paren">)</span> → float<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.predict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Predict label for the given features.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.predictRaw"> |
| <code class="sig-name descname">predictRaw</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.linalg.Vector.html#pyspark.ml.linalg.Vector" title="pyspark.ml.linalg.Vector">pyspark.ml.linalg.Vector</a></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.ml.linalg.Vector.html#pyspark.ml.linalg.Vector" title="pyspark.ml.linalg.Vector">pyspark.ml.linalg.Vector</a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.predictRaw" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Raw prediction for each possible label.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.read"> |
| <em class="property">classmethod </em><code class="sig-name descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.ml.util.JavaMLReader<span class="p">[</span>RL<span class="p">]</span><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.read" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an MLReader instance for this class.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save this ML instance to the given path, a shortcut of ‘write().save(path)’.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.set"> |
| <code class="sig-name descname">set</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.ml.param.Param.html#pyspark.ml.param.Param" title="pyspark.ml.param.Param">pyspark.ml.param.Param</a></span></em>, <em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.set" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets a parameter in the embedded param map.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.setFeaturesCol"> |
| <code class="sig-name descname">setFeaturesCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → P<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.setFeaturesCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.featuresCol" title="pyspark.ml.classification.LinearSVCModel.featuresCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">featuresCol</span></code></a>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.setPredictionCol"> |
| <code class="sig-name descname">setPredictionCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → P<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.setPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.predictionCol" title="pyspark.ml.classification.LinearSVCModel.predictionCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">predictionCol</span></code></a>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.setRawPredictionCol"> |
| <code class="sig-name descname">setRawPredictionCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → P<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.setRawPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.rawPredictionCol" title="pyspark.ml.classification.LinearSVCModel.rawPredictionCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">rawPredictionCol</span></code></a>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.setThreshold"> |
| <code class="sig-name descname">setThreshold</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel" title="pyspark.ml.classification.LinearSVCModel">pyspark.ml.classification.LinearSVCModel</a><a class="reference internal" href="../../_modules/pyspark/ml/classification.html#LinearSVCModel.setThreshold"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.setThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LinearSVCModel.threshold" title="pyspark.ml.classification.LinearSVCModel.threshold"><code class="xref py py-attr docutils literal notranslate"><span class="pre">threshold</span></code></a>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → <a class="reference internal" href="pyspark.ml.classification.LinearSVCTrainingSummary.html#pyspark.ml.classification.LinearSVCTrainingSummary" title="pyspark.ml.classification.LinearSVCTrainingSummary">pyspark.ml.classification.LinearSVCTrainingSummary</a><a class="reference internal" href="../../_modules/pyspark/ml/classification.html#LinearSVCModel.summary"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets summary (accuracy/precision/recall, objective history, total iterations) of model |
| trained on the training set. An exception is thrown if <cite>trainingSummary is None</cite>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.1.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.transform"> |
| <code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span><span class="p">:</span> <span class="n">pyspark.sql.dataframe.DataFrame</span></em>, <em class="sig-param"><span class="n">params</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>ParamMap<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span> → pyspark.sql.dataframe.DataFrame<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Transforms the input dataset with optional parameters.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.3.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>dataset</strong><span class="classifier"><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></span></dt><dd><p>input dataset</p> |
| </dd> |
| <dt><strong>params</strong><span class="classifier">dict, optional</span></dt><dd><p>an optional param map that overrides embedded params.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="../pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.DataFrame</span></code></a></dt><dd><p>transformed dataset</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.write"> |
| <code class="sig-name descname">write</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.ml.util.JavaMLWriter<a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.write" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an MLWriter instance for this ML instance.</p> |
| </dd></dl> |
| |
| <p class="rubric">Attributes Documentation</p> |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.aggregationDepth"> |
| <code class="sig-name descname">aggregationDepth</code><em class="property"> = Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.aggregationDepth" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.coefficients"> |
| <code class="sig-name descname">coefficients</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.coefficients" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model coefficients of Linear SVM Classifier.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.2.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.featuresCol"> |
| <code class="sig-name descname">featuresCol</code><em class="property"> = Param(parent='undefined', name='featuresCol', doc='features column name.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.featuresCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.fitIntercept"> |
| <code class="sig-name descname">fitIntercept</code><em class="property"> = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.fitIntercept" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.hasSummary"> |
| <code class="sig-name descname">hasSummary</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.hasSummary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Indicates whether a training summary exists for this model |
| instance.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.1.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.intercept"> |
| <code class="sig-name descname">intercept</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.intercept" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model intercept of Linear SVM Classifier.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.2.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.labelCol"> |
| <code class="sig-name descname">labelCol</code><em class="property"> = Param(parent='undefined', name='labelCol', doc='label column name.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.labelCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.maxBlockSizeInMB"> |
| <code class="sig-name descname">maxBlockSizeInMB</code><em class="property"> = Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.maxBlockSizeInMB" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.maxIter"> |
| <code class="sig-name descname">maxIter</code><em class="property"> = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.maxIter" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.numClasses"> |
| <code class="sig-name descname">numClasses</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.numClasses" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Number of classes (values which the label can take).</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.1.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.numFeatures"> |
| <code class="sig-name descname">numFeatures</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.numFeatures" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the number of features the model was trained on. If unknown, returns -1</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.1.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.params"> |
| <code class="sig-name descname">params</code><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns all params ordered by name. The default implementation |
| uses <code class="xref py py-func docutils literal notranslate"><span class="pre">dir()</span></code> to get all attributes of type |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">Param</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.predictionCol"> |
| <code class="sig-name descname">predictionCol</code><em class="property"> = Param(parent='undefined', name='predictionCol', doc='prediction column name.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.predictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.rawPredictionCol"> |
| <code class="sig-name descname">rawPredictionCol</code><em class="property"> = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.rawPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.regParam"> |
| <code class="sig-name descname">regParam</code><em class="property"> = Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.regParam" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.standardization"> |
| <code class="sig-name descname">standardization</code><em class="property"> = Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.standardization" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.threshold"> |
| <code class="sig-name descname">threshold</code><em class="property">: pyspark.ml.param.Param[float]</em><em class="property"> = Param(parent='undefined', name='threshold', doc='The threshold in binary classification applied to the linear model prediction. This threshold can be any real number, where Inf will make all predictions 0.0 and -Inf will make all predictions 1.0.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.threshold" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.tol"> |
| <code class="sig-name descname">tol</code><em class="property"> = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.tol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LinearSVCModel.weightCol"> |
| <code class="sig-name descname">weightCol</code><em class="property"> = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')</em><a class="headerlink" href="#pyspark.ml.classification.LinearSVCModel.weightCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
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| |
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