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<h2 title="Class ClassificationModel" class="title">Class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>></h2>
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<pre>public abstract class <span class="strong">ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>></span>
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<h4>ClassificationModel</h4>
<pre>public ClassificationModel()</pre>
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<h4>setRawPredictionCol</h4>
<pre>public <a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">M</a> setRawPredictionCol(java.lang.String value)</pre>
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<pre>public abstract int numClasses()</pre>
<div class="block">Number of classes (values which the label can take).</div>
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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>.
<p></div>
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<dt><strong>Overrides:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/PredictionModel.html#transform(org.apache.spark.sql.DataFrame)">transform</a></code> in class <code><a href="../../../../../org/apache/spark/ml/PredictionModel.html" title="class in org.apache.spark.ml">PredictionModel</a><<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">M</a> extends <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/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">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>predict</h4>
<pre>protected double predict(<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a> features)</pre>
<div class="block">Predict label for the given features.
This internal method is used to implement <code>transform()</code> and output <code>predictionCol</code>.
<p>
This default implementation for classification predicts the index of the maximum value
from <code>predictRaw()</code>.</div>
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<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/PredictionModel.html#predict(FeaturesType)">predict</a></code> in class <code><a href="../../../../../org/apache/spark/ml/PredictionModel.html" title="class in org.apache.spark.ml">PredictionModel</a><<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">M</a> extends <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/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a>,<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">M</a>>></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>features</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>protected abstract <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> predictRaw(<a href="../../../../../org/apache/spark/ml/classification/ClassificationModel.html" title="type parameter in ClassificationModel">FeaturesType</a> features)</pre>
<div class="block">Raw prediction for each possible label.
The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
a measure of confidence in each possible label (where larger = more confident).
This internal method is used to implement <code>transform()</code> and output <code>rawPredictionCol</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>vector where element i is the raw prediction for label i.
This raw prediction may be any real number, where a larger value indicates greater
confidence for that label.</dd></dl>
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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">Given a vector of raw predictions, select the predicted label.
This may be overridden to support thresholds which favor particular labels.</div>
<dl><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>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>
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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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