Generate indices to split data into training and test set. Returns the number of splitting iterations in the cross-validator. groups objectДЄlways ignored, exists for compatibility. y objectДЄlways ignored, exists for compatibility. Returns the number of splitting iterations in the cross-validator Parameters X objectДЄlways ignored, exists for compatibility. Get_n_splits ( X = None, y = None, groups = None ) В¶ Returns the number of splitting iterations in the cross-validator y_train, y_test = y, y TRAIN: TEST: TRAIN: TEST: TRAIN: TEST: print ( 'TRAIN:', train_index, 'TEST:', test_index ). y_train, y_test = y, y TRAIN: TEST: TRAIN: TEST: TRAIN: TEST: > # Add in a 2 period gap > tscv = TimeSeriesSplit ( n_splits = 3, test_size = 2, gap = 2 ) > for train_index, test_index in tscv. randint ( 0, 2, 12 ) > tscv = TimeSeriesSplit ( n_splits = 3, test_size = 2 ) > for train_index, test_index in tscv. y_train, y_test = y, y TRAIN: TEST: TRAIN: TEST: TRAIN: TEST: TRAIN: TEST: TRAIN: TEST: > # Fix test_size to 2 with 12 samples > X = np. array () > tscv = TimeSeriesSplit () > print ( tscv ) TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None) > for train_index, test_index in tscv.

import numpy as np > from sklearn.model_selection import TimeSeriesSplit > X = np.

Splitting by time tuneskit 3.0.3