Package smile.validation

Class RegressionValidation<M>

java.lang.Object

smile.validation.RegressionValidation<M>

Type Parameters:

M - the regression model type.

All Implemented Interfaces:

Serializable

public class RegressionValidation<M>
extends Object
implements Serializable

Regression model validation results.

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
final RegressionMetrics	metrics	The regression metrics.
final M	model	The model.
final double[]	prediction	The model prediction.
final double[]	truth	The true response variable of validation data.

Constructor Summary

Constructors

Constructor	Description
RegressionValidation(M model, double[] truth, double[] prediction, RegressionMetrics metrics)	Constructor.

Method Summary

Modifier and Type	Method	Description
static <M extends DataFrameRegression> RegressionValidation<M>	of(Formula formula, DataFrame train, DataFrame test, BiFunction<Formula,DataFrame,M> trainer)	Trains and validates a model on a train/validation split.
static <M extends DataFrameRegression> RegressionValidations<M>	of(Bag[] bags, Formula formula, DataFrame data, BiFunction<Formula,DataFrame,M> trainer)	Trains and validates a model on multiple train/validation split.
static <T, M extends Regression<T>> RegressionValidations<M>	of(Bag[] bags, T[] x, double[] y, BiFunction<T[],double[],M> trainer)	Trains and validates a model on multiple train/validation split.
static <T, M extends Regression<T>> RegressionValidation<M>	of(T[] x, double[] y, T[] testx, double[] testy, BiFunction<T[],double[],M> trainer)	Trains and validates a model on a train/validation split.
String	toString()

Methods inherited from class java.lang.Object

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

Field Details

model

public final M model

The model.

truth

public final double[] truth

The true response variable of validation data.

prediction

public final double[] prediction

The model prediction.

metrics

public final RegressionMetrics metrics

The regression metrics.

Constructor Details

RegressionValidation

public RegressionValidation(M model,
 double[] truth,
 double[] prediction,
 RegressionMetrics metrics)

Constructor.

Parameters:

model - the model.

truth - the ground truth.

prediction - the predictions.

metrics - the validation metrics.

Method Details

toString

public String toString()

Overrides:

toString in class Object

of

public static <T,
M extends Regression<T>>
RegressionValidation<M> of(T[] x,
 double[] y,
 T[] testx,
 double[] testy,
 BiFunction<T[],double[],M> trainer)

Trains and validates a model on a train/validation split.

Type Parameters:

T - the data type of samples.

M - the model type.

Parameters:

x - the training data.

y - the responsible variable of training data.

testx - the validation data.

testy - the responsible variable of validation data.

trainer - the lambda to train the model.

Returns:

the validation results.

of

public static <T,
M extends Regression<T>>
RegressionValidations<M> of(Bag[] bags,
 T[] x,
 double[] y,
 BiFunction<T[],double[],M> trainer)

Trains and validates a model on multiple train/validation split.

Type Parameters:

T - the data type of samples.

M - the model type.

Parameters:

bags - the data splits.

x - the training data.

y - the responsible variable.

trainer - the lambda to train the model.

Returns:

the validation results.

of

public static <M extends DataFrameRegression>
RegressionValidation<M> of(Formula formula,
 DataFrame train,
 DataFrame test,
 BiFunction<Formula,DataFrame,M> trainer)

Trains and validates a model on a train/validation split.

Type Parameters:

M - the model type.

Parameters:

formula - the model formula.

train - the training data.

test - the validation data.

trainer - the lambda to train the model.

Returns:

the validation results.

of

public static <M extends DataFrameRegression>
RegressionValidations<M> of(Bag[] bags,
 Formula formula,
 DataFrame data,
 BiFunction<Formula,DataFrame,M> trainer)

Trains and validates a model on multiple train/validation split.

Type Parameters:

M - the model type.

Parameters:

bags - the data splits.

formula - the model formula.

data - the data.

trainer - the lambda to train the model.

Returns:

the validation results.