First ML prediction (linear regression) 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 [1]
model = LinearRegression()

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

ADecisionTreeClassifier

BLogisticRegression

CLinearRegression

DKMeans

Attempts:

3 left

2fill in blank

medium

Complete the code to fit the linear regression model with features X and target y.

ML Python

model.[1](X, y)

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

Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

3fill in blank

hard

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

ML Python

predictions = model.[1](X_test)

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

Afit

Bscore

Ctransform

Dpredict

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)

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

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 predictions for each test sample with keys as sample indices.

ML Python

pred_dict = [1](i: [2] for i, [3] in enumerate(y_pred))

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

Adict

Bvalue

Ditem

Attempts:

3 left