Why proper evaluation prevents overfitting in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split data into training and testing sets.

ML Python

from sklearn.model_selection import [1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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Across_val_score

BStandardScaler

CGridSearchCV

Dtrain_test_split

Attempts:

3 left

2fill in blank

medium

Complete the code to train a model on the training data.

ML Python

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.[1](X_train, y_train)

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Apredict

Bfit

Cscore

Dtransform

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to evaluate the model correctly on test data.

ML Python

accuracy = model.[1](X_test, y_test)
print(f"Test accuracy: {accuracy:.2f}")

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Atransform

Bpredict

Cscore

Dfit

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of accuracy scores for training and testing data.

ML Python

scores = {
    'train_accuracy': model.[1](X_train, y_train),
    'test_accuracy': model.[2](X_test, y_test)
}

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Ascore

Bpredict

Cfit

Dtransform

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to perform cross-validation and print the mean accuracy.

ML Python

from sklearn.model_selection import [1]
scores = cross_val_score(model, X, y, cv=[2])
print(f"Mean CV accuracy: {scores.[3]():.2f}")

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Across_val_score

B5

Cmean

Dfit

Attempts:

3 left