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
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Constructor Summary
Constructors Constructor Description Builder()
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Method Summary
Modifier and Type Method Description DataframeEvaluationRegressionMetrics
build()
Builds aDataframeEvaluationRegressionMetrics
.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 tomse(Map)
, creating the map if needed.DataframeEvaluationRegressionMetrics.Builder
putRSquared(java.lang.String key, JsonData value)
Add a key/value torSquared(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
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Constructor Details
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Builder
public Builder()
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Method Details
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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
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putMse
Add a key/value tomse(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
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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
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huber
public DataframeEvaluationRegressionMetrics.Builder huber(@Nullable DataframeEvaluationRegressionMetricsHuber value)Pseudo Huber loss function.API name:
huber
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huber
public DataframeEvaluationRegressionMetrics.Builder huber(java.util.function.Function<DataframeEvaluationRegressionMetricsHuber.Builder,ObjectBuilder<DataframeEvaluationRegressionMetricsHuber>> fn)Pseudo Huber loss function.API name:
huber
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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
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putRSquared
public DataframeEvaluationRegressionMetrics.Builder putRSquared(java.lang.String key, JsonData value)Add a key/value torSquared(Map)
, creating the map if needed. -
build
Builds aDataframeEvaluationRegressionMetrics
.- Specified by:
build
in interfaceObjectBuilder<DataframeEvaluationRegressionMetrics>
- Throws:
java.lang.NullPointerException
- if some of the required fields are null.
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