【模型評估與選擇】sklearn.model_selection.KFold

1. 描述
KFold divides all the samples in k groups of samples, called folds (if k = n, this is equivalent to the Leave One Out strategy), of equal sizes (if possible). The prediction function is learned using k - 1 folds, and the fold left out is used for test.

2. 語法
sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

3. 引數:
1. n_splits: int, default=3
Number of folds. Must be at least 2.
將訓練/測試資料集劃分n_splits個互斥子集，每次用n_splits-1個子集的並集作為訓練集，餘下的子集作為測試集
2. shuffle:boolean, optional
Whether to shuffle the data before splitting into batches.
shuffle= False:不洗牌，每次執行結果相同，相當於random_state=整數
shuffle=True:洗牌，每次執行結果不同
shuffle=True和random_state=整數: 每次執行結果相同
3. random_state: int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used by np.random. Used when shuffle == True.

4. 方法
1. get_n_splits([X, y, groups])
Returns the number of splitting iterations in the cross-validator
2. split(X[, y, groups])
Generate indices to split data into training and test set.

注：Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

5. 例項

X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([1, 2, 3, 4])
kf = KFold(n_splits=3)
kf.get_n_splits(X)

print(kf)  

for train_index, test_index in kf.split(X):
   print("TRAIN:", train_index, "TEST:", test_index)
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]
print(X_train,y_train)
print(X_test,y_test)

KFold(n_splits=3, random_state=None, shuffle=False)
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1 3] TEST: [2]
TRAIN: [0 1 2] TEST: [3]
[[1 2]
[3 4]
[1 2]] [1 2 3]
[[3 4]] [4]

注：
The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_samples // n_splits, where n_samples is the number of samples.

擴充
StratifiedKFold
Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
GroupKFold
K-fold iterator variant with non-overlapping groups.
RepeatedKFold
Repeats K-Fold n times.