Linear regression concept in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple linear regression model using scikit-learn.

ML Python

from sklearn.linear_model import LinearRegression
model = LinearRegression([1])

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Afit_intercept=True

Bn_estimators=100

Crandom_state=42

Dactivation='relu'

Attempts:

3 left

2fill in blank

medium

Complete the code to train the linear regression model on data X and y.

ML Python

model.[1](X, y)

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Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to make predictions using the trained model.

ML Python

predictions = model.[1](X_test)

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Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

4fill in blank

hard

Fill both blanks to calculate the mean squared error between true and predicted values.

ML Python

from sklearn.metrics import [1]
mse = [2](y_true, y_pred)

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Amean_squared_error

Baccuracy_score

Cmean_absolute_error

Dr2_score

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of coefficients for each feature with their names.

ML Python

coef_dict = {{ [1]: [2] for [3], coef in zip(feature_names, model.coef_)}

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Afeature

Bcoef

Cfeature_names

Dmodel

Attempts:

3 left