Why regression predicts continuous values in ML Python - Test Your Understanding

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 [1]
model = [1]()

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ADecisionTreeClassifier

BLogisticRegression

CKNeighborsClassifier

DLinearRegression

Attempts:

3 left

2fill in blank

medium

Complete the code to fit the regression model on training data.

ML Python

model.fit(X_train, [1])

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Ay_train

BX_test

Cy_test

DX_train

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to predict continuous values using the trained model.

ML Python

predictions = model.[1](X_test)

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Apredict

Bpredict_proba

Cscore

Dfit

Attempts:

3 left

4fill in blank

hard

Fill both blanks to calculate the mean squared error (MSE) for regression predictions.

ML Python

from sklearn.metrics import [1]
mse = [2](y_test, predictions)

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Amean_squared_error

Baccuracy_score

Cconfusion_matrix

Droc_auc_score

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for each test sample with their true values.

ML Python

results = [1](zip([2], [3]))

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Adict

By_test

Cpredictions

Dlist

Attempts:

3 left