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| <div class="section" id="logisticregressionmodel"> |
| <h1>LogisticRegressionModel<a class="headerlink" href="#logisticregressionmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.ml.classification.</code><code class="sig-name descname">LogisticRegressionModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">java_model</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#LogisticRegressionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model fitted by LogisticRegression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.3.0.</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.LogisticRegressionModel.clear" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.copy" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.evaluate" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.explainParam" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.explainParams" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.extractParamMap" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getAggregationDepth" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getElasticNetParam" title="pyspark.ml.classification.LogisticRegressionModel.getElasticNetParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getElasticNetParam</span></code></a>()</p></td> |
| <td><p>Gets the value of elasticNetParam or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getFamily" title="pyspark.ml.classification.LogisticRegressionModel.getFamily"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getFamily</span></code></a>()</p></td> |
| <td><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.family" title="pyspark.ml.classification.LogisticRegressionModel.family"><code class="xref py py-attr docutils literal notranslate"><span class="pre">family</span></code></a> or its default value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getFeaturesCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getFitIntercept" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getLabelCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getLowerBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnCoefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getLowerBoundsOnCoefficients</span></code></a>()</p></td> |
| <td><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients"><code class="xref py py-attr docutils literal notranslate"><span class="pre">lowerBoundsOnCoefficients</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnIntercepts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getLowerBoundsOnIntercepts</span></code></a>()</p></td> |
| <td><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts"><code class="xref py py-attr docutils literal notranslate"><span class="pre">lowerBoundsOnIntercepts</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getMaxBlockSizeInMB" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getMaxIter" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getOrDefault" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getParam" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getProbabilityCol" title="pyspark.ml.classification.LogisticRegressionModel.getProbabilityCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getProbabilityCol</span></code></a>()</p></td> |
| <td><p>Gets the value of probabilityCol or its default value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getRawPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getRegParam" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getStandardization" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getThreshold" title="pyspark.ml.classification.LogisticRegressionModel.getThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getThreshold</span></code></a>()</p></td> |
| <td><p>Get threshold for binary classification.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getThresholds" title="pyspark.ml.classification.LogisticRegressionModel.getThresholds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getThresholds</span></code></a>()</p></td> |
| <td><p>If <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</span></code></a> is set, return its value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getTol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getUpperBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnCoefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getUpperBoundsOnCoefficients</span></code></a>()</p></td> |
| <td><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients"><code class="xref py py-attr docutils literal notranslate"><span class="pre">upperBoundsOnCoefficients</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnIntercepts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getUpperBoundsOnIntercepts</span></code></a>()</p></td> |
| <td><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts"><code class="xref py py-attr docutils literal notranslate"><span class="pre">upperBoundsOnIntercepts</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.getWeightCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.hasDefault" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.hasParam" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.isDefined" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.isSet" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.load" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predict" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predictProbability" title="pyspark.ml.classification.LogisticRegressionModel.predictProbability"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictProbability</span></code></a>(value)</p></td> |
| <td><p>Predict the probability of each class given the features.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.predictRaw" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.read" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.save" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.set" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.setFeaturesCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.featuresCol" title="pyspark.ml.classification.LogisticRegressionModel.featuresCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">featuresCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.setPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predictionCol" title="pyspark.ml.classification.LogisticRegressionModel.predictionCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">predictionCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.setProbabilityCol" title="pyspark.ml.classification.LogisticRegressionModel.setProbabilityCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setProbabilityCol</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.probabilityCol" title="pyspark.ml.classification.LogisticRegressionModel.probabilityCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probabilityCol</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.setRawPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.rawPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setThreshold" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.threshold" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setThresholds" title="pyspark.ml.classification.LogisticRegressionModel.setThresholds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setThresholds</span></code></a>(value)</p></td> |
| <td><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.transform" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.write" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.aggregationDepth" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.coefficientMatrix" title="pyspark.ml.classification.LogisticRegressionModel.coefficientMatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coefficientMatrix</span></code></a></p></td> |
| <td><p>Model coefficients.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.coefficients" title="pyspark.ml.classification.LogisticRegressionModel.coefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coefficients</span></code></a></p></td> |
| <td><p>Model coefficients of binomial logistic regression.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.elasticNetParam" title="pyspark.ml.classification.LogisticRegressionModel.elasticNetParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">elasticNetParam</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.family" title="pyspark.ml.classification.LogisticRegressionModel.family"><code class="xref py py-obj docutils literal notranslate"><span class="pre">family</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.featuresCol" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.fitIntercept" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.hasSummary" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.intercept" title="pyspark.ml.classification.LogisticRegressionModel.intercept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intercept</span></code></a></p></td> |
| <td><p>Model intercept of binomial logistic regression.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.interceptVector" title="pyspark.ml.classification.LogisticRegressionModel.interceptVector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interceptVector</span></code></a></p></td> |
| <td><p>Model intercept.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.labelCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.lowerBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lowerBoundsOnCoefficients</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lowerBoundsOnIntercepts</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.maxBlockSizeInMB" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.maxIter" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.numClasses" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.numFeatures" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.params" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.probabilityCol" title="pyspark.ml.classification.LogisticRegressionModel.probabilityCol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">probabilityCol</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.rawPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.regParam" title="pyspark.ml.classification.LogisticRegressionModel.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-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.standardization" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.summary" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.threshold" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">thresholds</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.tol" title="pyspark.ml.classification.LogisticRegressionModel.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-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">upperBoundsOnCoefficients</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">upperBoundsOnIntercepts</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.weightCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.clear"> |
| <code class="sig-name descname">clear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.copy"> |
| <code class="sig-name descname">copy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.evaluate"> |
| <code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/ml/classification.html#LogisticRegressionModel.evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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 2.0.0.</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.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.LogisticRegressionModel.explainParam"> |
| <code class="sig-name descname">explainParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.explainParams"> |
| <code class="sig-name descname">explainParams</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.extractParamMap"> |
| <code class="sig-name descname">extractParamMap</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">extra</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getAggregationDepth"> |
| <code class="sig-name descname">getAggregationDepth</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getElasticNetParam"> |
| <code class="sig-name descname">getElasticNetParam</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getElasticNetParam" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of elasticNetParam or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getFamily"> |
| <code class="sig-name descname">getFamily</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getFamily" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.family" title="pyspark.ml.classification.LogisticRegressionModel.family"><code class="xref py py-attr docutils literal notranslate"><span class="pre">family</span></code></a> or its default value.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.1.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getFeaturesCol"> |
| <code class="sig-name descname">getFeaturesCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getFitIntercept"> |
| <code class="sig-name descname">getFitIntercept</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getLabelCol"> |
| <code class="sig-name descname">getLabelCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getLowerBoundsOnCoefficients"> |
| <code class="sig-name descname">getLowerBoundsOnCoefficients</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnCoefficients" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients"><code class="xref py py-attr docutils literal notranslate"><span class="pre">lowerBoundsOnCoefficients</span></code></a></p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.3.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnIntercepts"> |
| <code class="sig-name descname">getLowerBoundsOnIntercepts</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getLowerBoundsOnIntercepts" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts"><code class="xref py py-attr docutils literal notranslate"><span class="pre">lowerBoundsOnIntercepts</span></code></a></p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.3.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getMaxBlockSizeInMB"> |
| <code class="sig-name descname">getMaxBlockSizeInMB</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getMaxIter"> |
| <code class="sig-name descname">getMaxIter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getOrDefault"> |
| <code class="sig-name descname">getOrDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getParam"> |
| <code class="sig-name descname">getParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getPredictionCol"> |
| <code class="sig-name descname">getPredictionCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getProbabilityCol"> |
| <code class="sig-name descname">getProbabilityCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getProbabilityCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of probabilityCol or its default value.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getRawPredictionCol"> |
| <code class="sig-name descname">getRawPredictionCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getRegParam"> |
| <code class="sig-name descname">getRegParam</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getStandardization"> |
| <code class="sig-name descname">getStandardization</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getThreshold"> |
| <code class="sig-name descname">getThreshold</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get threshold for binary classification.</p> |
| <p>If <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</span></code></a> is set with length 2 (i.e., binary classification), |
| this returns the equivalent threshold: |
| <span class="math notranslate nohighlight">\(\frac{1}{1 + \frac{thresholds(0)}{thresholds(1)}}\)</span>. |
| Otherwise, returns <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.threshold" title="pyspark.ml.classification.LogisticRegressionModel.threshold"><code class="xref py py-attr docutils literal notranslate"><span class="pre">threshold</span></code></a> if set or its default value if unset.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getThresholds"> |
| <code class="sig-name descname">getThresholds</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getThresholds" title="Permalink to this definition">¶</a></dt> |
| <dd><p>If <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</span></code></a> is set, return its value. |
| Otherwise, if <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.threshold" title="pyspark.ml.classification.LogisticRegressionModel.threshold"><code class="xref py py-attr docutils literal notranslate"><span class="pre">threshold</span></code></a> is set, return the equivalent thresholds for binary |
| classification: (1-threshold, threshold). |
| If neither are set, throw an error.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getTol"> |
| <code class="sig-name descname">getTol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.getUpperBoundsOnCoefficients"> |
| <code class="sig-name descname">getUpperBoundsOnCoefficients</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnCoefficients" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients"><code class="xref py py-attr docutils literal notranslate"><span class="pre">upperBoundsOnCoefficients</span></code></a></p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.3.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnIntercepts"> |
| <code class="sig-name descname">getUpperBoundsOnIntercepts</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.getUpperBoundsOnIntercepts" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Gets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts" title="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts"><code class="xref py py-attr docutils literal notranslate"><span class="pre">upperBoundsOnIntercepts</span></code></a></p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.3.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.getWeightCol"> |
| <code class="sig-name descname">getWeightCol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.hasDefault"> |
| <code class="sig-name descname">hasDefault</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.hasParam"> |
| <code class="sig-name descname">hasParam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">paramName</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.isDefined"> |
| <code class="sig-name descname">isDefined</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.isSet"> |
| <code class="sig-name descname">isSet</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.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></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predict"> |
| <code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.predictProbability"> |
| <code class="sig-name descname">predictProbability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.predictProbability" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Predict the probability of each class given the 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.LogisticRegressionModel.predictRaw"> |
| <code class="sig-name descname">predictRaw</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.read"> |
| <em class="property">classmethod </em><code class="sig-name descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.set"> |
| <code class="sig-name descname">set</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">param</span></em>, <em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setFeaturesCol"> |
| <code class="sig-name descname">setFeaturesCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setFeaturesCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.featuresCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setPredictionCol"> |
| <code class="sig-name descname">setPredictionCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.predictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setProbabilityCol"> |
| <code class="sig-name descname">setProbabilityCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setProbabilityCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.probabilityCol" title="pyspark.ml.classification.LogisticRegressionModel.probabilityCol"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probabilityCol</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.LogisticRegressionModel.setRawPredictionCol"> |
| <code class="sig-name descname">setRawPredictionCol</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setRawPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.rawPredictionCol" title="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.setThreshold"> |
| <code class="sig-name descname">setThreshold</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.threshold" title="pyspark.ml.classification.LogisticRegressionModel.threshold"><code class="xref py py-attr docutils literal notranslate"><span class="pre">threshold</span></code></a>. |
| Clears value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</span></code></a> if it has been set.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.setThresholds"> |
| <code class="sig-name descname">setThresholds</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.setThresholds" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the value of <a class="reference internal" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="pyspark.ml.classification.LogisticRegressionModel.thresholds"><code class="xref py py-attr docutils literal notranslate"><span class="pre">thresholds</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.LogisticRegressionModel.transform"> |
| <code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">params</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.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.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.LogisticRegressionModel.write"> |
| <code class="sig-name descname">write</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.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.LogisticRegressionModel.aggregationDepth" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.coefficientMatrix"> |
| <code class="sig-name descname">coefficientMatrix</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.coefficientMatrix" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model coefficients.</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.LogisticRegressionModel.coefficients"> |
| <code class="sig-name descname">coefficients</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.coefficients" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model coefficients of binomial logistic regression. |
| An exception is thrown in the case of multinomial logistic regression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 2.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.elasticNetParam"> |
| <code class="sig-name descname">elasticNetParam</code><em class="property"> = Param(parent='undefined', name='elasticNetParam', doc='the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.elasticNetParam" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.family"> |
| <code class="sig-name descname">family</code><em class="property"> = Param(parent='undefined', name='family', doc='The name of family which is a description of the label distribution to be used in the model. Supported options: auto, binomial, multinomial')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.family" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.featuresCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.fitIntercept" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.hasSummary"> |
| <code class="sig-name descname">hasSummary</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.intercept"> |
| <code class="sig-name descname">intercept</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.intercept" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model intercept of binomial logistic regression. |
| An exception is thrown in the case of multinomial logistic regression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.interceptVector"> |
| <code class="sig-name descname">interceptVector</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.interceptVector" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Model intercept.</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.LogisticRegressionModel.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.LogisticRegressionModel.labelCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients"> |
| <code class="sig-name descname">lowerBoundsOnCoefficients</code><em class="property"> = Param(parent='undefined', name='lowerBoundsOnCoefficients', doc='The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnCoefficients" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts"> |
| <code class="sig-name descname">lowerBoundsOnIntercepts</code><em class="property"> = Param(parent='undefined', name='lowerBoundsOnIntercepts', doc='The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must beequal with 1 for binomial regression, or the number oflasses for multinomial regression.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.lowerBoundsOnIntercepts" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.maxBlockSizeInMB" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.maxIter" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.numClasses"> |
| <code class="sig-name descname">numClasses</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.numFeatures"> |
| <code class="sig-name descname">numFeatures</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.params"> |
| <code class="sig-name descname">params</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.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.LogisticRegressionModel.predictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.probabilityCol"> |
| <code class="sig-name descname">probabilityCol</code><em class="property"> = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.probabilityCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.rawPredictionCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.regParam" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.standardization" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.summary"> |
| <code class="sig-name descname">summary</code><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.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 2.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.threshold"> |
| <code class="sig-name descname">threshold</code><em class="property"> = Param(parent='undefined', name='threshold', doc='Threshold in binary classification prediction, in range [0, 1]. If threshold and thresholds are both set, they must match.e.g. if threshold is p, then thresholds must be equal to [1-p, p].')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.threshold" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.thresholds"> |
| <code class="sig-name descname">thresholds</code><em class="property"> = Param(parent='undefined', name='thresholds', doc="Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.")</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.thresholds" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.tol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients"> |
| <code class="sig-name descname">upperBoundsOnCoefficients</code><em class="property"> = Param(parent='undefined', name='upperBoundsOnCoefficients', doc='The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnCoefficients" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts"> |
| <code class="sig-name descname">upperBoundsOnIntercepts</code><em class="property"> = Param(parent='undefined', name='upperBoundsOnIntercepts', doc='The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.')</em><a class="headerlink" href="#pyspark.ml.classification.LogisticRegressionModel.upperBoundsOnIntercepts" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.ml.classification.LogisticRegressionModel.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.LogisticRegressionModel.weightCol" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
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