org.apache.spark.ml.evaluation

Class RegressionEvaluator

Object

org.apache.spark.ml.evaluation.Evaluator

org.apache.spark.ml.evaluation.RegressionEvaluator

All Implemented Interfaces:

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

public final class RegressionEvaluator
extends Evaluator
implements HasPredictionCol, HasLabelCol, HasWeightCol, DefaultParamsWritable

Evaluator for regression, which expects two input columns: prediction and label.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RegressionEvaluator()
RegressionEvaluator(String uid)

Method Summary

Modifier and Type	Method and Description
RegressionEvaluator	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()
boolean	getThroughOrigin()
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 RegressionEvaluator	load(String path)
Param<String>	metricName() Param for metric name in evaluation.
Param<String>	predictionCol() Param for prediction column name.
static MLReader<T>	read()
RegressionEvaluator	setLabelCol(String value)
RegressionEvaluator	setMetricName(String value)
RegressionEvaluator	setPredictionCol(String value)
RegressionEvaluator	setThroughOrigin(boolean value)
RegressionEvaluator	setWeightCol(String value)
BooleanParam	throughOrigin() param for whether the regression is through the origin.
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, toString, wait, wait, wait

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

getPredictionCol

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.Identifiable

toString

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

write

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

save

Constructor Detail

RegressionEvaluator

public RegressionEvaluator(String uid)

RegressionEvaluator

public RegressionEvaluator()

Method Detail

load

public static RegressionEvaluator 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)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

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: - "rmse" (default): root mean squared error - "mse": mean squared error - "r2": R^2^ metric - "mae": mean absolute error - "var": explained variance

Returns:

(undocumented)

getMetricName

public String getMetricName()

setMetricName

public RegressionEvaluator setMetricName(String value)

throughOrigin

public BooleanParam throughOrigin()

param for whether the regression is through the origin. Default: false.

Returns:

(undocumented)

getThroughOrigin

public boolean getThroughOrigin()

setThroughOrigin

public RegressionEvaluator setThroughOrigin(boolean value)

setPredictionCol

public RegressionEvaluator setPredictionCol(String value)

setLabelCol

public RegressionEvaluator setLabelCol(String value)

setWeightCol

public RegressionEvaluator 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

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 RegressionEvaluator 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)