Package co.elastic.clients.elasticsearch.ml

Class DataframeEvaluationRegressionMetrics.Builder

java.lang.Object

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

All Implemented Interfaces:

ObjectBuilder<DataframeEvaluationRegressionMetrics>

Enclosing class:

DataframeEvaluationRegressionMetrics

public static class DataframeEvaluationRegressionMetrics.Builder
extends java.lang.Object
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.util.Map<java.lang.String,JsonData> value)	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	putMse(java.lang.String key, JsonData value)	Add a key/value to mse(Map), creating the map if needed.
DataframeEvaluationRegressionMetrics.Builder	putRSquared(java.lang.String key, JsonData value)	Add a key/value to rSquared(Map), creating the map if needed.
DataframeEvaluationRegressionMetrics.Builder	rSquared(java.util.Map<java.lang.String,JsonData> value)	Proportion of the variance in the dependent variable that is predictable from the independent variables.

Methods inherited from class java.lang.Object

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

Constructor Details

Builder

public Builder()

Method Details

mse

public DataframeEvaluationRegressionMetrics.Builder mse(@Nullable
java.util.Map<java.lang.String,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

putMse

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

Add a key/value to mse(Map), creating the map if needed.

msle

public 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 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 DataframeEvaluationRegressionMetrics.Builder huber(@Nullable
DataframeEvaluationRegressionMetricsHuber value)

Pseudo Huber loss function.

API name: huber

huber

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

Pseudo Huber loss function.

API name: huber

rSquared

public DataframeEvaluationRegressionMetrics.Builder rSquared(@Nullable
java.util.Map<java.lang.String,JsonData> value)

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

API name: r_squared

putRSquared

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

Add a key/value to rSquared(Map), creating the map if needed.

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.