Linear regression with scikit-learn in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LinearRegression class from scikit-learn.

ML Python

from sklearn.linear_model import [1]

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

ALinearRegression

BLogisticRegression

CDecisionTreeClassifier

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a classifier class instead of LinearRegression.

Using clustering classes like KMeans.

The LinearRegression class is imported from sklearn.linear_model to create a linear regression model.

2fill in blank

medium

Complete the code to create a LinearRegression model instance.

ML Python

model = [1]()

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

ARandomForestRegressor

BSVC

CLinearRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using classifier classes instead of regression.

Using unrelated model classes.

We create an instance of LinearRegression to train and predict with a linear model.

3fill in blank

hard

Fix the error in the code to train the 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

💡 Hint

Common Mistakes

Using predict() instead of fit() to train.

Using transform() which is for preprocessing.

The fit() method trains the model using the input features X and target y.

4fill in blank

hard

Fill both blanks to predict target values and calculate the model's R² score.

ML Python

predictions = model.[1](X_test)
score = model.[2](X_test, y_test)

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

Apredict

Bscore

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() instead of predict() or score().

Using transform() which is not for prediction or scoring.

Use predict() to get predictions from the test features, and score() to calculate the R² score comparing predictions to true values.

5fill in blank

hard

Fill all three blanks to create a dictionary of coefficients, intercept, and R² score after training.

ML Python

results = {
    'coefficients': model.[1],
    'intercept': model.[2],
    'r2_score': model.[3](X_test, y_test)
}

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

Acoef_

Bintercept_

Cscore

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of score for R².

Confusing attribute names with method names.

After training, model.coef_ gives coefficients, model.intercept_ gives intercept, and model.score() computes R² score.

Practice

1. What is the main goal of linear regression in machine learning?

easy

A. To find a line that predicts a continuous value from input data

B. To classify data into categories

C. To cluster data points into groups

D. To reduce the number of features in data

Solution

1. Step 1: Understand the purpose of linear regression

   Linear regression aims to model the relationship between input variables and a continuous output by fitting a line.

2. Step 2: Compare with other tasks

   Classification assigns categories, clustering groups data, and feature reduction simplifies data, which are different tasks.

3. Final Answer:

   To find a line that predicts a continuous value from input data -> Option A

4. Quick Check:

   Linear regression predicts continuous values [OK]

Hint: Linear regression predicts continuous numbers, not categories [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking linear regression clusters data
• Believing it reduces features

2. Which of the following is the correct way to import the LinearRegression class from scikit-learn?

easy

A. from sklearn.linear_model import LinearRegression

B. import LinearRegression from sklearn.linear_model

C. from sklearn import LinearRegression

D. import linear_model.LinearRegression

Solution

1. Step 1: Recall correct Python import syntax

   Python imports classes using 'from module import ClassName' syntax.

2. Step 2: Match with scikit-learn structure

   LinearRegression is inside sklearn.linear_model, so correct import is from sklearn.linear_model import LinearRegression.

3. Final Answer:

   from sklearn.linear_model import LinearRegression -> Option A

4. Quick Check:

   Correct import syntax = from sklearn.linear_model import LinearRegression [OK]

Hint: Use 'from sklearn.linear_model import LinearRegression' [OK]

Common Mistakes:

• Using wrong import order
• Trying to import directly from sklearn
• Using incorrect module names

3. What will be the output of the following code snippet?

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression()
model.fit(X, y)
print(round(model.coef_[0], 1))

medium

A. 0.5

B. 2.0

C. 10.0

D. 1.0

Solution

1. Step 1: Understand the data relationship

   X values are 1 to 5, y values are exactly double X (2,4,6,8,10), so slope should be 2.

2. Step 2: Check model coefficient

   LinearRegression fits y = coef * X + intercept. coef_ will be close to 2.0 here.

3. Final Answer:

   2.0 -> Option B

4. Quick Check:

   Slope of y=2x is 2.0 [OK]

Hint: Slope equals change in y over change in x [OK]

Common Mistakes:

• Confusing coefficient with intercept
• Not rounding output
• Assuming coefficient is 1 or 10

4. Identify the error in this code that tries to train a linear regression model:

from sklearn.linear_model import LinearRegression
X = [[1], [2], [3]]
y = [2, 4, 6]
model = LinearRegression()
model.fit(X, y)
print(model.predict([4]))

medium

A. Passing a list instead of numpy array to fit

B. LinearRegression class is not instantiated correctly

C. Missing import for numpy

D. Predict input shape is incorrect, should be 2D array

Solution

1. Step 1: Check input shapes for fit and predict

   fit accepts list of lists for X, so no error there. But predict expects 2D array, [4] is 1D.

2. Step 2: Fix predict input shape

   Predict should get [[4]] (2D) not [4] (1D) to avoid shape error.

3. Final Answer:

   Predict input shape is incorrect, should be 2D array -> Option D

4. Quick Check:

   Predict input must be 2D array [OK]

Hint: Always pass 2D array to predict, e.g., [[value]] [OK]

Common Mistakes:

• Passing 1D array to predict
• Assuming fit needs numpy arrays
• Wrong class instantiation

5. You trained a linear regression model with scikit-learn and got an R² score of 0.85 on test data. What does this score mean?

hard

A. The model has 85% chance to predict correctly

B. The model's predictions are 85% accurate

C. The model explains 85% of the variance in the target variable

D. The model's error rate is 15% on test data

Solution

1. Step 1: Understand R² score meaning

   R² (coefficient of determination) measures how well the model explains variance in the target.

2. Step 2: Interpret 0.85 R²

   A score of 0.85 means 85% of the variance in the target is explained by the model.

3. Final Answer:

   The model explains 85% of the variance in the target variable -> Option C

4. Quick Check:

   R² = variance explained, not accuracy or error rate [OK]

Hint: R² shows variance explained, not accuracy percentage [OK]

Common Mistakes:

• Confusing R² with accuracy
• Thinking R² is probability
• Assuming R² is error rate