Package co.elastic.clients.elasticsearch.ml

Class DataframeEvaluationRegressionMetrics.Builder

java.lang.Object

co.elastic.clients.util.ObjectBuilderBase

co.elastic.clients.util.WithJsonObjectBuilderBase<DataframeEvaluationRegressionMetrics.Builder>

co.elastic.clients.elasticsearch.ml.DataframeEvaluationRegressionMetrics.Builder

All Implemented Interfaces:

WithJson<DataframeEvaluationRegressionMetrics.Builder>, ObjectBuilder<DataframeEvaluationRegressionMetrics>

Enclosing class:

DataframeEvaluationRegressionMetrics

public static class DataframeEvaluationRegressionMetrics.Builder
extends WithJsonObjectBuilderBase<DataframeEvaluationRegressionMetrics.Builder>
implements ObjectBuilder<DataframeEvaluationRegressionMetrics>

Builder for DataframeEvaluationRegressionMetrics.

Constructor Summary

Constructors

Constructor	Description
Builder()

Method Summary

Modifier and Type	Method	Description
DataframeEvaluationRegressionMetrics	build()	Builds a DataframeEvaluationRegressionMetrics.
DataframeEvaluationRegressionMetrics.Builder	huber(DataframeEvaluationRegressionMetricsHuber value)	Pseudo Huber loss function.
DataframeEvaluationRegressionMetrics.Builder	huber(java.util.function.Function<DataframeEvaluationRegressionMetricsHuber.Builder,ObjectBuilder<DataframeEvaluationRegressionMetricsHuber>> fn)	Pseudo Huber loss function.
DataframeEvaluationRegressionMetrics.Builder	mse(java.lang.String key, JsonData value)	Average squared difference between the predicted values and the actual (ground truth) value.
DataframeEvaluationRegressionMetrics.Builder	mse(java.util.Map<java.lang.String,JsonData> map)	Average squared difference between the predicted values and the actual (ground truth) value.
DataframeEvaluationRegressionMetrics.Builder	msle(DataframeEvaluationRegressionMetricsMsle value)	Average squared difference between the logarithm of the predicted values and the logarithm of the actual (ground truth) value.
DataframeEvaluationRegressionMetrics.Builder	msle(java.util.function.Function<DataframeEvaluationRegressionMetricsMsle.Builder,ObjectBuilder<DataframeEvaluationRegressionMetricsMsle>> fn)	Average squared difference between the logarithm of the predicted values and the logarithm of the actual (ground truth) value.
DataframeEvaluationRegressionMetrics.Builder	rSquared(java.lang.String key, JsonData value)	Proportion of the variance in the dependent variable that is predictable from the independent variables.
DataframeEvaluationRegressionMetrics.Builder	rSquared(java.util.Map<java.lang.String,JsonData> map)	Proportion of the variance in the dependent variable that is predictable from the independent variables.
protected DataframeEvaluationRegressionMetrics.Builder	self()

Methods inherited from class co.elastic.clients.util.WithJsonObjectBuilderBase

withJson

Methods inherited from class co.elastic.clients.util.ObjectBuilderBase

_checkSingleUse, _listAdd, _listAddAll, _mapPut, _mapPutAll

Methods inherited from class java.lang.Object

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

Methods inherited from interface co.elastic.clients.json.WithJson

withJson, withJson

Constructor Details

Builder

public Builder()

Method Details

mse

public final DataframeEvaluationRegressionMetrics.Builder mse(java.util.Map<java.lang.String,JsonData> map)

Average squared difference between the predicted values and the actual (ground truth) value. For more information, read this wiki article.

API name: mse

Adds all entries of map to mse.

mse

public final DataframeEvaluationRegressionMetrics.Builder mse(java.lang.String key,
JsonData value)

Average squared difference between the predicted values and the actual (ground truth) value. For more information, read this wiki article.

API name: mse

Adds an entry to mse.

msle

public final DataframeEvaluationRegressionMetrics.Builder msle(@Nullable
DataframeEvaluationRegressionMetricsMsle value)

Average squared difference between the logarithm of the predicted values and the logarithm of the actual (ground truth) value.

API name: msle

msle

public final DataframeEvaluationRegressionMetrics.Builder msle(java.util.function.Function<DataframeEvaluationRegressionMetricsMsle.Builder,ObjectBuilder<DataframeEvaluationRegressionMetricsMsle>> fn)

Average squared difference between the logarithm of the predicted values and the logarithm of the actual (ground truth) value.

API name: msle

huber

public final DataframeEvaluationRegressionMetrics.Builder huber(@Nullable
DataframeEvaluationRegressionMetricsHuber value)

Pseudo Huber loss function.

API name: huber

huber

public final DataframeEvaluationRegressionMetrics.Builder huber(java.util.function.Function<DataframeEvaluationRegressionMetricsHuber.Builder,ObjectBuilder<DataframeEvaluationRegressionMetricsHuber>> fn)

Pseudo Huber loss function.

API name: huber

rSquared

public final DataframeEvaluationRegressionMetrics.Builder rSquared(java.util.Map<java.lang.String,JsonData> map)

Proportion of the variance in the dependent variable that is predictable from the independent variables.

API name: r_squared

Adds all entries of map to rSquared.

rSquared

public final DataframeEvaluationRegressionMetrics.Builder rSquared(java.lang.String key,
JsonData value)

Proportion of the variance in the dependent variable that is predictable from the independent variables.

API name: r_squared

Adds an entry to rSquared.

self

protected DataframeEvaluationRegressionMetrics.Builder self()

Specified by:

self in class WithJsonObjectBuilderBase<DataframeEvaluationRegressionMetrics.Builder>

build

public DataframeEvaluationRegressionMetrics build()

Builds a DataframeEvaluationRegressionMetrics.

Specified by:

build in interface ObjectBuilder<DataframeEvaluationRegressionMetrics>

Throws:

java.lang.NullPointerException - if some of the required fields are null.