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])

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

Afit_intercept=True

Bn_estimators=100

Crandom_state=42

Dactivation='relu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using parameters meant for other models like random_state or n_estimators.

Forgetting to include the intercept can cause wrong predictions.

The fit_intercept=True parameter tells the model to calculate the intercept (bias) term. This is essential for linear regression.

2fill in blank

medium

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

Calling transform which is not a method of LinearRegression.

The fit method trains the model by finding the best line that fits the data.

3fill in blank

hard

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

ML Python

predictions = model.[1](X_test)

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

Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Calling fit again instead of predict.

Using transform which is not available for LinearRegression.

The predict method is used to get the model's output on new data.

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

💡 Hint

Common Mistakes

Using accuracy_score which is for classification.

Confusing mean_absolute_error with mean_squared_error.

The mean_squared_error function calculates the average squared difference between true and predicted values, a common error metric for regression.

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_)}

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

Afeature

Bcoef

Cfeature_names

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Using model or feature_names as keys instead of the loop variable.

Mixing up coef and feature in the comprehension.

This dictionary comprehension pairs each feature name with its coefficient from the model.

Practice

1. What does linear regression primarily do in machine learning?

easy

A. It groups data points into clusters.

B. It reduces the number of features in data.

C. It finds a straight line to predict values based on input data.

D. It creates decision trees for classification.

Solution

1. Step 1: Understand the goal of linear regression

   Linear regression aims to find a straight line that best fits the data points to predict continuous values.

2. Step 2: Compare with other methods

   Clustering groups data, feature reduction simplifies data, and decision trees classify data, which are different tasks.

3. Final Answer:

   It finds a straight line to predict values based on input data. -> Option C

4. Quick Check:

   Linear regression = straight line prediction [OK]

Hint: Linear regression fits a line to predict numbers [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking it groups data instead of predicting
• Mixing it up with feature reduction techniques

2. Which of the following is the correct formula for a simple linear regression model?

easy

A. y = m - x / b

B. y = mx + b

C. y = m + x * b

D. y = x / m + b

Solution

1. Step 1: Recall the linear regression formula

   The formula is y = mx + b, where m is slope and b is intercept.

2. Step 2: Check each option

   Only y = mx + b matches the correct formula exactly.

3. Final Answer:

   y = mx + b -> Option B

4. Quick Check:

   Linear formula = y = mx + b [OK]

Hint: Slope times x plus intercept equals y [OK]

Common Mistakes:

• Mixing multiplication and division signs
• Swapping slope and intercept positions
• Using incorrect operators between terms

3. Given the linear regression model y = 2x + 3, what is the predicted value when x = 4?

medium

A. 11

B. 14

C. 8

D. 10

Solution

1. Step 1: Substitute x = 4 into the model

   Calculate y = 2 * 4 + 3.

2. Step 2: Perform the calculation

   2 * 4 = 8, then 8 + 3 = 11.

3. Final Answer:

   11 -> Option A

4. Quick Check:

   2*4+3 = 11 [OK]

Hint: Multiply slope by x, then add intercept [OK]

Common Mistakes:

• Adding before multiplying
• Using wrong slope or intercept values
• Confusing x with y

4. Identify the error in this Python code for linear regression prediction:

def predict(x):
    return 2 * x +

print(predict(5))

medium

A. Syntax error due to incomplete return statement

B. Missing intercept value in the formula

C. Wrong function name for prediction

D. Incorrect multiplication operator

Solution

1. Step 1: Check the return statement syntax

   The return line ends with a plus sign and no value after it, causing a syntax error.

2. Step 2: Confirm error type

   Python requires complete expressions; incomplete return causes syntax error.

3. Final Answer:

   Syntax error due to incomplete return statement -> Option A

4. Quick Check:

   Incomplete return line = syntax error [OK]

Hint: Check if return line ends abruptly [OK]

Common Mistakes:

• Ignoring incomplete expression errors
• Assuming missing intercept is the error
• Thinking function name matters here

5. You have data points: (1,3), (2,5), (3,7). Which linear regression model best fits this data?

hard

A. y = x + 2

B. y = 3x + 0

C. y = 2x + 3

D. y = 2x + 1

Solution

1. Step 1: Calculate slope (m)

   Change in y: 5-3=2, 7-5=2; change in x: 2-1=1, 3-2=1; slope m = 2/1 = 2.

2. Step 2: Calculate intercept (b)

   Use point (1,3): 3 = 2*1 + b -> b = 3 - 2 = 1.

3. Final Answer:

   y = 2x + 1 -> Option D

4. Quick Check:

   Slope=2, intercept=1 fits points [OK]

Hint: Slope = change in y/change in x, intercept from point [OK]

Common Mistakes:

• Mixing slope and intercept values
• Using wrong points for calculation
• Assuming intercept is zero without checking