How should you complete the code segment?

HOTSPOT

You are evaluating a Python NumPy array that contains six data points defined as follows:

data = [10, 20, 30, 40, 50, 60]

You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

train: [10 40 50 60], test: [20 30]

train: [20 30 40 60], test: [10 50]

train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.

How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

View Answer

Answer: Explanation:

Box 1: k-fold

Box 2: 3

K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.

Box 3: data

Example: Example:

>>>

>>> from sklearn.model_selection import KFold

>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])

>>> y = np.array([1, 2, 3, 4])

>>> kf = KFold(n_splits=2)

>>> kf.get_n_splits(X)

2

>>> print(kf)

KFold(n_splits=2, random_state=None, shuffle=False)

>>> 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]

TRAIN: [2 3] TEST: [0 1]

TRAIN: [0 1] TEST: [2 3]

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html