Linear regression concept in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the purpose of linear regression

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

A To reduce the number of features by combining them

B To classify data points into different categories based on input features

C To cluster data points into groups without labels

D To find the best straight line that predicts a continuous output from input features

❓ Predict Output

intermediate

Output of simple linear regression prediction

What is the predicted value y when x=3 using the model y = 2x + 1?

ML Python

def predict(x):
    return 2 * x + 1

result = predict(3)
print(result)

A 8

B 6

C 7

D 5

❓ Hyperparameter

advanced

Effect of learning rate in linear regression training

What happens if the learning rate is set too high during linear regression training with gradient descent?

A The model will underfit the data by stopping early

B The model may overshoot the minimum loss and fail to converge

C The model will ignore the input features

D The model will always converge faster without any issues

❓ Metrics

advanced

Choosing the right metric for linear regression

Which metric is commonly used to measure the error of a linear regression model's predictions?

A Mean Squared Error (MSE)

B Accuracy

C F1 Score

D Confusion Matrix

🔧 Debug

expert

Identifying the error in linear regression code

What error will this code raise when fitting a linear regression model using scikit-learn? from sklearn.linear_model import LinearRegression X = [[1], [2], [3]] y = [2, 4, 6] model = LinearRegression() model.fit(X, y) print(model.predict([4]))

A ValueError because input to predict must be 2D array, but [4] is 1D

B TypeError because model.fit expects a list of strings

C No error, output will be 8

D NameError because LinearRegression is not imported