smile.validation

Interface Bootstrap

public interface Bootstrap

The bootstrap is a general tool for assessing statistical accuracy. The basic idea is to randomly draw samples with replacement from the training data, each samples the same size as the original training set. This is done many times (say k = 100), producing k bootstrap datasets. Then we refit the model to each of the bootstrap datasets and examine the behavior of the fits over the k replications.

Method Summary

Modifier and Type	Method and Description
static <M extends DataFrameClassifier> ClassificationValidations<M>	classification(int k, smile.data.formula.Formula formula, smile.data.DataFrame data, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,M> trainer) Runs classification bootstrap validation.
static <T,M extends Classifier<T>> ClassificationValidations<M>	classification(int k, T[] x, int[] y, java.util.function.BiFunction<T[],int[],M> trainer) Runs classification bootstrap validation.
static Bag[]	of(int[] category, int k) Stratified bootstrap sampling.
static Bag[]	of(int n, int k) Bootstrap sampling.
static <M extends DataFrameRegression> RegressionValidations<M>	regression(int k, smile.data.formula.Formula formula, smile.data.DataFrame data, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,M> trainer) Runs regression bootstrap validation.
static <T,M extends Regression<T>> RegressionValidations<M>	regression(int k, T[] x, double[] y, java.util.function.BiFunction<T[],double[],M> trainer) Runs regression bootstrap validation.

Method Detail

of

static Bag[] of(int n,
                int k)

Bootstrap sampling.

Parameters:

n - the number of samples.

k - the number of rounds of bootstrap.

of

static Bag[] of(int[] category,
                int k)

Stratified bootstrap sampling.

Parameters:

category - the strata labels.

k - the number of rounds of bootstrap.

classification

static <T,M extends Classifier<T>> ClassificationValidations<M> classification(int k,
                                                                               T[] x,
                                                                               int[] y,
                                                                               java.util.function.BiFunction<T[],int[],M> trainer)

Runs classification bootstrap validation.

Parameters:

k - k-fold bootstrap sampling.

x - the samples.

y - the sample labels.

trainer - the lambda to train a model.

Returns:

the validation results.

classification

static <M extends DataFrameClassifier> ClassificationValidations<M> classification(int k,
                                                                                   smile.data.formula.Formula formula,
                                                                                   smile.data.DataFrame data,
                                                                                   java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,M> trainer)

Runs classification bootstrap validation.

Parameters:

k - k-fold bootstrap sampling.

formula - the model specification.

data - the training/validation data.

trainer - the lambda to train a model.

Returns:

the validation results.

regression

static <T,M extends Regression<T>> RegressionValidations<M> regression(int k,
                                                                       T[] x,
                                                                       double[] y,
                                                                       java.util.function.BiFunction<T[],double[],M> trainer)

Runs regression bootstrap validation.

Parameters:

k - k-fold bootstrap sampling.

x - the samples.

y - the response variable.

trainer - the lambda to train a model.

Returns:

the validation results.

regression

static <M extends DataFrameRegression> RegressionValidations<M> regression(int k,
                                                                           smile.data.formula.Formula formula,
                                                                           smile.data.DataFrame data,
                                                                           java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,M> trainer)

Runs regression bootstrap validation.

Parameters:

k - k-fold bootstrap sampling.

formula - the model specification.

data - the training/validation data.

trainer - the lambda to train a model.

Returns:

the validation results.