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<div class="subTitle">org.apache.spark.ml.classification</div>
<h2 title="Class ProbabilisticClassificationModel" class="title">Class ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>></h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
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<li><a href="../../../../../org/apache/spark/ml/PipelineStage.html" title="class in org.apache.spark.ml">org.apache.spark.ml.PipelineStage</a></li>
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<li><a href="../../../../../org/apache/spark/ml/Model.html" title="class in org.apache.spark.ml">org.apache.spark.ml.Model</a><M></li>
<li>
<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/ml/PredictionModel.html" title="class in org.apache.spark.ml">org.apache.spark.ml.PredictionModel</a><FeaturesType,M></li>
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<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="class in org.apache.spark.ml.classification">org.apache.spark.ml.classification.ClassificationModel</a><FeaturesType,M></li>
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<li>org.apache.spark.ml.classification.ProbabilisticClassificationModel<FeaturesType,M></li>
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<dt>All Implemented Interfaces:</dt>
<dd>java.io.Serializable, <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a>, <a href="../../../../../org/apache/spark/ml/param/Params.html" title="interface in org.apache.spark.ml.param">Params</a>, <a href="../../../../../org/apache/spark/ml/util/Identifiable.html" title="interface in org.apache.spark.ml.util">Identifiable</a></dd>
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<dt>Direct Known Subclasses:</dt>
<dd><a href="../../../../../org/apache/spark/ml/classification/DecisionTreeClassificationModel.html" title="class in org.apache.spark.ml.classification">DecisionTreeClassificationModel</a>, <a href="../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html" title="class in org.apache.spark.ml.classification">LogisticRegressionModel</a>, <a href="../../../../../org/apache/spark/ml/classification/NaiveBayesModel.html" title="class in org.apache.spark.ml.classification">NaiveBayesModel</a>, <a href="../../../../../org/apache/spark/ml/classification/RandomForestClassificationModel.html" title="class in org.apache.spark.ml.classification">RandomForestClassificationModel</a></dd>
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<pre>public abstract class <span class="strong">ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>></span>
extends <a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="class in org.apache.spark.ml.classification">ClassificationModel</a><FeaturesType,M></pre>
<div class="block">:: DeveloperApi ::
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Model produced by a <a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassifier.html" title="class in org.apache.spark.ml.classification"><code>ProbabilisticClassifier</code></a>.
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<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.ml.classification.ProbabilisticClassificationModel">Serialized Form</a></dd></dl>
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<th class="colOne" scope="col">Constructor and Description</th>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#ProbabilisticClassificationModel()">ProbabilisticClassificationModel</a></strong>()</code> </td>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<td class="colFirst"><code>static void</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#normalizeToProbabilitiesInPlace(org.apache.spark.mllib.linalg.DenseVector)">normalizeToProbabilitiesInPlace</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/DenseVector.html" title="class in org.apache.spark.mllib.linalg">DenseVector</a> v)</code>
<div class="block">Normalize a vector of raw predictions to be a multinomial probability vector, in place.</div>
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<td class="colFirst"><code>protected <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#predictProbability(FeaturesType)">predictProbability</a></strong>(<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a> features)</code>
<div class="block">Predict the probability of each class given the features.</div>
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<td class="colFirst"><code>protected double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#probability2prediction(org.apache.spark.mllib.linalg.Vector)">probability2prediction</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> probability)</code>
<div class="block">Given a vector of class conditional probabilities, select the predicted label.</div>
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<td class="colFirst"><code>protected double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#raw2prediction(org.apache.spark.mllib.linalg.Vector)">raw2prediction</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</code>
<div class="block">Given a vector of raw predictions, select the predicted label.</div>
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<td class="colFirst"><code>protected <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#raw2probability(org.apache.spark.mllib.linalg.Vector)">raw2probability</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</code>
<div class="block">Non-in-place version of <code>raw2probabilityInPlace()</code></div>
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<td class="colFirst"><code>protected abstract <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#raw2probabilityInPlace(org.apache.spark.mllib.linalg.Vector)">raw2probabilityInPlace</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</code>
<div class="block">Estimate the probability of each class given the raw prediction,
doing the computation in-place.</div>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#setProbabilityCol(java.lang.String)">setProbabilityCol</a></strong>(java.lang.String value)</code> </td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html#setThresholds(double[])">setThresholds</a></strong>(double[] value)</code> </td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a></code></td>
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<div class="block">Transforms dataset by reading from <code>featuresCol</code>, and appending new columns as specified by
parameters:
- predicted labels as <code>predictionCol</code> of type <code>Double</code>
- raw predictions (confidences) as <code>rawPredictionCol</code> of type <code>Vector</code>
- probability of each class as <code>probabilityCol</code> of type <code>Vector</code>.</div>
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<code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#numClasses()">numClasses</a>, <a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#predict(FeaturesType)">predict</a>, <a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#predictRaw(FeaturesType)">predictRaw</a>, <a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#setRawPredictionCol(java.lang.String)">setRawPredictionCol</a></code></li>
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<h4>ProbabilisticClassificationModel</h4>
<pre>public ProbabilisticClassificationModel()</pre>
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<h4>normalizeToProbabilitiesInPlace</h4>
<pre>public static void normalizeToProbabilitiesInPlace(<a href="../../../../../org/apache/spark/mllib/linalg/DenseVector.html" title="class in org.apache.spark.mllib.linalg">DenseVector</a> v)</pre>
<div class="block">Normalize a vector of raw predictions to be a multinomial probability vector, in place.
<p>
The input raw predictions should be >= 0.
The output vector sums to 1, unless the input vector is all-0 (in which case the output is
all-0 too).
<p>
NOTE: This is NOT applicable to all models, only ones which effectively use class
instance counts for raw predictions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>v</code> - (undocumented)</dd></dl>
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<h4>setProbabilityCol</h4>
<pre>public <a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a> setProbabilityCol(java.lang.String value)</pre>
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<h4>setThresholds</h4>
<pre>public <a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a> setThresholds(double[] value)</pre>
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<h4>transform</h4>
<pre>public <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> transform(<a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql">DataFrame</a> dataset)</pre>
<div class="block">Transforms dataset by reading from <code>featuresCol</code>, and appending new columns as specified by
parameters:
- predicted labels as <code>predictionCol</code> of type <code>Double</code>
- raw predictions (confidences) as <code>rawPredictionCol</code> of type <code>Vector</code>
- probability of each class as <code>probabilityCol</code> of type <code>Vector</code>.
<p></div>
<dl>
<dt><strong>Overrides:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#transform(org.apache.spark.sql.DataFrame)">transform</a></code> in class <code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="class in org.apache.spark.ml.classification">ClassificationModel</a><<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a> extends <a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="class in org.apache.spark.ml.classification">ProbabilisticClassificationModel</a><<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a>>></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>dataset</code> - input dataset</dd>
<dt><span class="strong">Returns:</span></dt><dd>transformed dataset</dd></dl>
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<h4>raw2probabilityInPlace</h4>
<pre>protected abstract <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> raw2probabilityInPlace(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</pre>
<div class="block">Estimate the probability of each class given the raw prediction,
doing the computation in-place.
These predictions are also called class conditional probabilities.
<p>
This internal method is used to implement <code>transform()</code> and output <code>probabilityCol</code>.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>rawPrediction</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>Estimated class conditional probabilities (modified input vector)</dd></dl>
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<h4>raw2probability</h4>
<pre>protected <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> raw2probability(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</pre>
<div class="block">Non-in-place version of <code>raw2probabilityInPlace()</code></div>
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<pre>protected double raw2prediction(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> rawPrediction)</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#raw2prediction(org.apache.spark.mllib.linalg.Vector)">ClassificationModel</a></code></strong></div>
<div class="block">Given a vector of raw predictions, select the predicted label.
This may be overridden to support thresholds which favor particular labels.</div>
<dl>
<dt><strong>Overrides:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html#raw2prediction(org.apache.spark.mllib.linalg.Vector)">raw2prediction</a></code> in class <code><a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="class in org.apache.spark.ml.classification">ClassificationModel</a><<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a> extends <a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="class in org.apache.spark.ml.classification">ProbabilisticClassificationModel</a><<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">M</a>>></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>rawPrediction</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>predicted label</dd></dl>
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<pre>protected <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> predictProbability(<a href="../../../../../org/apache/spark/ml/classification/ProbabilisticClassificationModel.html" title="type parameter in ProbabilisticClassificationModel">FeaturesType</a> features)</pre>
<div class="block">Predict the probability of each class given the features.
These predictions are also called class conditional probabilities.
<p>
This internal method is used to implement <code>transform()</code> and output <code>probabilityCol</code>.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>features</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>Estimated class conditional probabilities</dd></dl>
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<pre>protected double probability2prediction(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> probability)</pre>
<div class="block">Given a vector of class conditional probabilities, select the predicted label.
This supports thresholds which favor particular labels.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>probability</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>predicted label</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/sql/types/StructType.html" title="class in org.apache.spark.sql.types">StructType</a> validateAndTransformSchema(<a href="../../../../../org/apache/spark/sql/types/StructType.html" title="class in org.apache.spark.sql.types">StructType</a> schema,
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<pre>public <a href="../../../../../org/apache/spark/sql/types/StructType.html" title="class in org.apache.spark.sql.types">StructType</a> validateAndTransformSchema(<a href="../../../../../org/apache/spark/sql/types/StructType.html" title="class in org.apache.spark.sql.types">StructType</a> schema,
boolean fitting,
<a href="../../../../../org/apache/spark/sql/types/DataType.html" title="class in org.apache.spark.sql.types">DataType</a> featuresDataType)</pre>
<div class="block">Validates and transforms the input schema with the provided param map.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>schema</code> - input schema</dd><dd><code>fitting</code> - whether this is in fitting</dd><dd><code>featuresDataType</code> - SQL DataType for FeaturesType.
E.g., <a href="../../../../../org/apache/spark/mllib/linalg/VectorUDT.html" title="class in org.apache.spark.mllib.linalg"><code>VectorUDT</code></a> for vector features.</dd>
<dt><span class="strong">Returns:</span></dt><dd>output schema</dd></dl>
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