Cross-validation (K-fold) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KFold class from scikit-learn.

ML Python

from sklearn.model_selection import [1]

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AKFold

Btrain_test_split

Ccross_val_score

DGridSearchCV

Attempts:

3 left

2fill in blank

medium

Complete the code to create a KFold object with 5 splits.

ML Python

kf = KFold(n_splits=[1])

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A3

B5

C10

D1

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to correctly split data using KFold.

ML Python

for train_index, test_index in kf.[1](X):
    X_train, X_test = X[train_index], X[test_index]

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Atransform

Bfit

Cfit_transform

Dsplit

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of train and test indices for each fold.

ML Python

folds = {i: {'train': train_idx, 'test': test_idx} for i, (train_idx, [1]) in enumerate(kf.[2](X))}

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Atest_idx

Btrain_idx

Csplit

Dfit

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to perform K-fold cross-validation scoring with accuracy metric.

ML Python

scores = cross_val_score([1], X, y, cv=[2], scoring=[3])

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Amodel

Bkf

C'accuracy'

D'neg_mean_squared_error'

Attempts:

3 left