Package org.apache.spark.ml.evaluation
Class BinaryClassificationEvaluator
Object
org.apache.spark.ml.evaluation.Evaluator
org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
- All Implemented Interfaces:
Serializable
,Params
,HasLabelCol
,HasRawPredictionCol
,HasWeightCol
,DefaultParamsWritable
,Identifiable
,MLWritable
,scala.Serializable
public class BinaryClassificationEvaluator
extends Evaluator
implements HasRawPredictionCol, HasLabelCol, HasWeightCol, DefaultParamsWritable
Evaluator for binary classification, which expects input columns rawPrediction, label and
an optional weight column.
The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1)
or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
- See Also:
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionCreates a copy of this instance with the same UID and some extra params.double
Evaluates model output and returns a scalar metric.getMetrics
(Dataset<?> dataset) Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.int
boolean
Indicates whether the metric returned byevaluate
should be maximized (true, default) or minimized (false).labelCol()
Param for label column name.param for metric name in evaluation (supports"areaUnderROC"
(default),"areaUnderPR"
)numBins()
param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation.Param for raw prediction (a.k.a. confidence) column name.static MLReader<T>
read()
setLabelCol
(String value) setMetricName
(String value) setNumBins
(int value) setRawPredictionCol
(String value) setWeightCol
(String value) toString()
uid()
An immutable unique ID for the object and its derivatives.Param for weight column name.Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
write
Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol
getLabelCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasRawPredictionCol
getRawPredictionCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol
getWeightCol
Methods inherited from interface org.apache.spark.ml.util.MLWritable
save
Methods inherited from interface org.apache.spark.ml.param.Params
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
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Constructor Details
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BinaryClassificationEvaluator
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BinaryClassificationEvaluator
public BinaryClassificationEvaluator()
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Method Details
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load
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read
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weightCol
Description copied from interface:HasWeightCol
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.- Specified by:
weightCol
in interfaceHasWeightCol
- Returns:
- (undocumented)
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labelCol
Description copied from interface:HasLabelCol
Param for label column name.- Specified by:
labelCol
in interfaceHasLabelCol
- Returns:
- (undocumented)
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rawPredictionCol
Description copied from interface:HasRawPredictionCol
Param for raw prediction (a.k.a. confidence) column name.- Specified by:
rawPredictionCol
in interfaceHasRawPredictionCol
- Returns:
- (undocumented)
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uid
Description copied from interface:Identifiable
An immutable unique ID for the object and its derivatives.- Specified by:
uid
in interfaceIdentifiable
- Returns:
- (undocumented)
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metricName
param for metric name in evaluation (supports"areaUnderROC"
(default),"areaUnderPR"
)- Returns:
- (undocumented)
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getMetricName
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setMetricName
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numBins
param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation. If 0, no down-sampling will occur. Default: 1000.- Returns:
- (undocumented)
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getNumBins
public int getNumBins() -
setNumBins
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setRawPredictionCol
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setLabelCol
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setWeightCol
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evaluate
Description copied from class:Evaluator
Evaluates model output and returns a scalar metric. The value ofEvaluator.isLargerBetter()
specifies whether larger values are better. -
getMetrics
Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.- Parameters:
dataset
- a dataset that contains labels/observations and predictions.- Returns:
- BinaryClassificationMetrics
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isLargerBetter
public boolean isLargerBetter()Description copied from class:Evaluator
Indicates whether the metric returned byevaluate
should be maximized (true, default) or minimized (false). A given evaluator may support multiple metrics which may be maximized or minimized.- Overrides:
isLargerBetter
in classEvaluator
- Returns:
- (undocumented)
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copy
Description copied from interface:Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. SeedefaultCopy()
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toString
- Specified by:
toString
in interfaceIdentifiable
- Overrides:
toString
in classObject
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