Package co.elastic.clients.elasticsearch.ml

Class DataframeEvaluationRegressionMetrics

java.lang.Object

co.elastic.clients.elasticsearch.ml.DataframeEvaluationRegressionMetrics

All Implemented Interfaces:

JsonpSerializable

@JsonpDeserializable
public final class DataframeEvaluationRegressionMetrics
extends java.lang.Object
implements JsonpSerializable

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	DataframeEvaluationRegressionMetrics.Builder	Builder for DataframeEvaluationRegressionMetrics.

Field Summary

Fields

Modifier and Type	Field	Description
static JsonpDeserializer<DataframeEvaluationRegressionMetrics>	_DESERIALIZER	Json deserializer for DataframeEvaluationRegressionMetrics

Constructor Summary

Constructors

Constructor	Description
DataframeEvaluationRegressionMetrics(DataframeEvaluationRegressionMetrics.Builder builder)
DataframeEvaluationRegressionMetrics(java.util.function.Function<DataframeEvaluationRegressionMetrics.Builder,DataframeEvaluationRegressionMetrics.Builder> fn)

Method Summary

Modifier and Type	Method	Description
DataframeEvaluationRegressionMetricsHuber	huber()	Pseudo Huber loss function.
java.util.Map<java.lang.String,JsonData>	mse()	Average squared difference between the predicted values and the actual (ground truth) value.
DataframeEvaluationRegressionMetricsMsle	msle()	Average squared difference between the logarithm of the predicted values and the logarithm of the actual (ground truth) value.
java.util.Map<java.lang.String,JsonData>	rSquared()	Proportion of the variance in the dependent variable that is predictable from the independent variables.
void	serialize(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)	Serialize this object to JSON.
protected void	serializeInternal(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
protected static void	setupDataframeEvaluationRegressionMetricsDeserializer(DelegatingDeserializer<DataframeEvaluationRegressionMetrics.Builder> op)

Methods inherited from class java.lang.Object

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

Field Details

_DESERIALIZER

public static final JsonpDeserializer<DataframeEvaluationRegressionMetrics> _DESERIALIZER

Json deserializer for DataframeEvaluationRegressionMetrics

Constructor Details

DataframeEvaluationRegressionMetrics

public DataframeEvaluationRegressionMetrics(DataframeEvaluationRegressionMetrics.Builder builder)

DataframeEvaluationRegressionMetrics

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

Method Details

mse

@Nullable
public java.util.Map<java.lang.String,JsonData> mse()

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

API name: mse

msle

@Nullable
public DataframeEvaluationRegressionMetricsMsle msle()

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

API name: msle

huber

@Nullable
public DataframeEvaluationRegressionMetricsHuber huber()

Pseudo Huber loss function.

API name: huber

rSquared

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

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

API name: r_squared

serialize

public void serialize(jakarta.json.stream.JsonGenerator generator,
JsonpMapper mapper)

Serialize this object to JSON.

Specified by:

serialize in interface JsonpSerializable

serializeInternal

protected void serializeInternal(jakarta.json.stream.JsonGenerator generator,
JsonpMapper mapper)

setupDataframeEvaluationRegressionMetricsDeserializer

protected static void setupDataframeEvaluationRegressionMetricsDeserializer(DelegatingDeserializer<DataframeEvaluationRegressionMetrics.Builder> op)