smile.validation

Interface LOOCV

public interface LOOCV

Leave-one-out cross validation. LOOCV uses a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. This is the same as a K-fold cross-validation with K being equal to the number of observations in the original sample. Leave-one-out cross-validation is usually very expensive from a computational point of view because of the large number of times the training process is repeated.

Method Summary

Modifier and Type	Method and Description
static ClassificationMetrics	classification(smile.data.formula.Formula formula, smile.data.DataFrame data, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameClassifier> trainer) Runs leave-one-out cross validation tests.
static <T,M extends Classifier<T>> ClassificationMetrics	classification(T[] x, int[] y, java.util.function.BiFunction<T[],int[],M> trainer) Runs leave-one-out cross validation tests.
static int[][]	of(int n) Returns the training sample index for each round.
static RegressionMetrics	regression(smile.data.formula.Formula formula, smile.data.DataFrame data, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameRegression> trainer) Runs leave-one-out cross validation tests.
static <T,M extends Regression<T>> RegressionMetrics	regression(T[] x, double[] y, java.util.function.BiFunction<T[],double[],M> trainer) Runs leave-one-out cross validation tests.

Method Detail

of

static int[][] of(int n)

Returns the training sample index for each round.

Parameters:

n - the number of samples.

Returns:

The index of training instances for each round. The left one of i-th round is i-th sample.

classification

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

Runs leave-one-out cross validation tests.

Returns:

the predictions.

classification

static ClassificationMetrics classification(smile.data.formula.Formula formula,
                                            smile.data.DataFrame data,
                                            java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameClassifier> trainer)

Runs leave-one-out cross validation tests.

Returns:

the predictions.

regression

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

Runs leave-one-out cross validation tests.

Returns:

the predictions.

regression

static RegressionMetrics regression(smile.data.formula.Formula formula,
                                    smile.data.DataFrame data,
                                    java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameRegression> trainer)

Runs leave-one-out cross validation tests.

Returns:

the predictions.