Split indices 0..N-1 into k folds for cross-validation. Return a list of (train_idx, val_idx) pairs where each index appears exactly once in validation across all folds.
K-Fold Partitioning:
{0,1,…,N−1}=F1∪F2∪⋯∪Fkwhere Fi∩Fj=∅ for i≠j, and ∣Fi∣−∣Fj∣≤1 for all i, j.
For fold i:
vali=Fitraini=j=i⋃FjN: int - total number of samplesk: int - number of foldsshuffle: bool - whether to shuffle before splittingrng: np.random.Generator or None - random generator for reproducibilityInput: N=5, k=2, shuffle=False
Output: [(train=[3,4], val=[0,1,2]), (train=[0,1,2], val=[3,4])]
5 items split into 2 folds of sizes [3, 2]. Each fold uses one part as validation, the rest as training.
Input: N=7, k=3, shuffle=False
Output: [(train=[3,4,5,6], val=[0,1,2]), (train=[0,1,2,5,6], val=[3,4]), (train=[0,1,2,3,4], val=[5,6])]
7 items → 3 folds of sizes [3, 2, 2]. Larger folds come first (7 mod 3 = 1 extra item in fold 1).
Create np.arange(), optionally shuffle, then split into k contiguous chunks using np.array_split().
For fold i, validation is fold i, training is np.concatenate() of all other folds.
(train_idx, val_idx)rng.permutation() if rng provided, else np.random.shuffle()Sign in to take notes on this problem
Accepts: number
Accepts: number
Accepts: any
Split indices 0..N-1 into k folds for cross-validation. Return a list of (train_idx, val_idx) pairs where each index appears exactly once in validation across all folds.
K-Fold Partitioning:
{0,1,…,N−1}=F1∪F2∪⋯∪Fkwhere Fi∩Fj=∅ for i≠j, and ∣Fi∣−∣Fj∣≤1 for all i, j.
For fold i:
vali=Fitraini=j=i⋃FjN: int - total number of samplesk: int - number of foldsshuffle: bool - whether to shuffle before splittingrng: np.random.Generator or None - random generator for reproducibilityInput: N=5, k=2, shuffle=False
Output: [(train=[3,4], val=[0,1,2]), (train=[0,1,2], val=[3,4])]
5 items split into 2 folds of sizes [3, 2]. Each fold uses one part as validation, the rest as training.
Input: N=7, k=3, shuffle=False
Output: [(train=[3,4,5,6], val=[0,1,2]), (train=[0,1,2,5,6], val=[3,4]), (train=[0,1,2,3,4], val=[5,6])]
7 items → 3 folds of sizes [3, 2, 2]. Larger folds come first (7 mod 3 = 1 extra item in fold 1).
Create np.arange(), optionally shuffle, then split into k contiguous chunks using np.array_split().
For fold i, validation is fold i, training is np.concatenate() of all other folds.
(train_idx, val_idx)rng.permutation() if rng provided, else np.random.shuffle()Sign in to take notes on this problem
Accepts: number
Accepts: number
Accepts: any