Elastic Net regularization in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the ElasticNet model from scikit-learn.

ML Python

from sklearn.linear_model import [1]

Drag options to blanks, or click blank then click option'

AElasticNet

BLinearRegression

CLogisticRegression

DRidge

Attempts:

3 left

💡 Hint

Common Mistakes

Importing LinearRegression instead of ElasticNet.

Confusing ElasticNet with LogisticRegression.

The ElasticNet model is imported from sklearn.linear_model using ElasticNet.

2fill in blank

medium

Complete the code to create an ElasticNet model with alpha set to 0.5.

ML Python

model = ElasticNet([1]=0.5)

Drag options to blanks, or click blank then click option'

Aalpha

Bmax_iter

Cl1_ratio

Dfit_intercept

Attempts:

3 left

💡 Hint

Common Mistakes

Using l1_ratio instead of alpha for regularization strength.

Setting max_iter instead of alpha.

The alpha parameter controls the overall strength of regularization in ElasticNet.

3fill in blank

hard

Fix the error in the code to fit the ElasticNet model on training data X_train and y_train.

ML Python

model = ElasticNet(alpha=0.1, l1_ratio=0.7)
model.[1](X_train, y_train)

Drag options to blanks, or click blank then click option'

Ascore

Bfit

Ctransform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is not available for ElasticNet.

The fit method trains the model on the given data.

4fill in blank

hard

Fill both blanks to create a dictionary of coefficients and intercept from the trained ElasticNet model.

ML Python

model = ElasticNet(alpha=0.2, l1_ratio=0.5).fit(X, y)
result = {"coefficients": model.[1], "intercept": model.[2]

Drag options to blanks, or click blank then click option'

Acoef_

Bintercept_

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit or predict instead of coef_ or intercept_.

Confusing attribute names without underscores.

The trained model stores coefficients in coef_ and intercept in intercept_.

5fill in blank

hard

Fill all three blanks to compute the mean squared error (MSE) of ElasticNet predictions on test data.

ML Python

from sklearn.metrics import {{BLANK_1 }}
predictions = model.predict(X_test)
mse = [2](y_test, {{BLANK_3}})

Drag options to blanks, or click blank then click option'

Amean_squared_error

Bpredictions

Cy_test

Dr2_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using r2_score instead of mean_squared_error.

Swapping true and predicted values in the function.

We import mean_squared_error to measure error. The function takes true values y_test and predicted values predictions.