org.apache.spark.ml.evaluation

Class Evaluator

Object

org.apache.spark.ml.evaluation.Evaluator

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable

Direct Known Subclasses:

BinaryClassificationEvaluator, ClusteringEvaluator, MulticlassClassificationEvaluator, RegressionEvaluator

public abstract class Evaluator
extends Object
implements Params

:: DeveloperApi :: Abstract class for evaluators that compute metrics from predictions.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Evaluator()

Method Summary

Modifier and Type	Method and Description
abstract Evaluator	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
abstract double	evaluate(Dataset<?> dataset) Evaluates model output and returns a scalar metric.
double	evaluate(Dataset<?> dataset, ParamMap paramMap) Evaluates model output and returns a scalar metric.
boolean	isLargerBetter() Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false).

Methods inherited from class Object

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

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.Identifiable

toString, uid

Constructor Detail

Evaluator

public Evaluator()

Method Detail

copy

public abstract Evaluator 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

Parameters:

extra - (undocumented)

Returns:

(undocumented)

evaluate

public double evaluate(Dataset<?> dataset,
                       ParamMap paramMap)

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

Parameters:

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

paramMap - parameter map that specifies the input columns and output metrics

Returns:

metric

evaluate

public abstract double evaluate(Dataset<?> dataset)

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

Parameters:

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

Returns:

metric

isLargerBetter

public boolean isLargerBetter()

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.

Returns:

(undocumented)