org.apache.spark.ml.evaluation

Class BinaryClassificationEvaluator

Object

org.apache.spark.ml.evaluation.Evaluator

org.apache.spark.ml.evaluation.BinaryClassificationEvaluator

All Implemented Interfaces:

java.io.Serializable, Params, HasLabelCol, HasRawPredictionCol, HasWeightCol, DefaultParamsWritable, Identifiable, MLWritable

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:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BinaryClassificationEvaluator()
BinaryClassificationEvaluator(String uid)

Method Summary

Modifier and Type	Method and Description
BinaryClassificationEvaluator	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
double	evaluate(Dataset<?> dataset) Evaluates model output and returns a scalar metric.
String	getMetricName()
BinaryClassificationMetrics	getMetrics(Dataset<?> dataset) Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.
int	getNumBins()
boolean	isLargerBetter() Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false).
Param<String>	labelCol() Param for label column name.
static BinaryClassificationEvaluator	load(String path)
Param<String>	metricName() param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")
IntParam	numBins() param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation.
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
static MLReader<T>	read()
BinaryClassificationEvaluator	setLabelCol(String value)
BinaryClassificationEvaluator	setMetricName(String value)
BinaryClassificationEvaluator	setNumBins(int value)
BinaryClassificationEvaluator	setRawPredictionCol(String value)
BinaryClassificationEvaluator	setWeightCol(String value)
String	toString()
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	weightCol() Param for weight column name.

Methods inherited from class org.apache.spark.ml.evaluation.Evaluator

evaluate, params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.shared.HasRawPredictionCol

getRawPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol

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, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Constructor Detail

BinaryClassificationEvaluator

public BinaryClassificationEvaluator(String uid)

BinaryClassificationEvaluator

public BinaryClassificationEvaluator()

Method Detail

load

public static BinaryClassificationEvaluator load(String path)

read

public static MLReader<T> read()

weightCol

public final Param<String> 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 interface HasWeightCol

Returns:

(undocumented)

labelCol

public final Param<String> labelCol()

Description copied from interface: HasLabelCol

Param for label column name.

Specified by:

labelCol in interface HasLabelCol

Returns:

(undocumented)

rawPredictionCol

public final Param<String> rawPredictionCol()

Description copied from interface: HasRawPredictionCol

Param for raw prediction (a.k.a. confidence) column name.

Specified by:

rawPredictionCol in interface HasRawPredictionCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

metricName

public Param<String> metricName()

param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

Returns:

(undocumented)

getMetricName

public String getMetricName()

setMetricName

public BinaryClassificationEvaluator setMetricName(String value)

numBins

public IntParam 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)

getNumBins

public int getNumBins()

setNumBins

public BinaryClassificationEvaluator setNumBins(int value)

setRawPredictionCol

public BinaryClassificationEvaluator setRawPredictionCol(String value)

setLabelCol

public BinaryClassificationEvaluator setLabelCol(String value)

setWeightCol

public BinaryClassificationEvaluator setWeightCol(String value)

evaluate

public double evaluate(Dataset<?> dataset)

Description copied from class: Evaluator

Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.

Specified by:

evaluate in class Evaluator

Parameters:

dataset - a dataset that contains labels/observations and predictions.

Returns:

metric

getMetrics

public BinaryClassificationMetrics getMetrics(Dataset<?> dataset)

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

isLargerBetter

public boolean isLargerBetter()

Description copied from class: Evaluator

Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false). A given evaluator may support multiple metrics which may be maximized or minimized.

Overrides:

isLargerBetter in class Evaluator

Returns:

(undocumented)

copy

public BinaryClassificationEvaluator copy(ParamMap extra)

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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Evaluator

Parameters:

extra - (undocumented)

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object