Linear regression with scikit-learn in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Linear Regression Model Training

What is the printed output of the following code snippet after training a linear regression model?

ML Python

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], 2), round(model.intercept_, 2))

A 1.0 0.0

B 1.0 1.0

C 2.0 1.0

D 2.0 0.0

❓ Model Choice

intermediate

Choosing the Correct Model for Linear Regression

Which scikit-learn class should you use to perform a simple linear regression on a dataset?

A sklearn.cluster.KMeans

B sklearn.tree.DecisionTreeClassifier

C sklearn.linear_model.LinearRegression

D sklearn.neural_network.MLPClassifier

❓ Hyperparameter

advanced

Effect of fit_intercept Parameter in LinearRegression

What is the effect of setting fit_intercept=False when creating a LinearRegression model in scikit-learn?

A The model will not calculate an intercept term and will force the line through the origin.

B The model will use L1 regularization to reduce overfitting.

C The model will automatically normalize the input features before fitting.

D The model will fit a polynomial regression instead of linear.

❓ Metrics

advanced

Interpreting R² Score for Linear Regression

After training a linear regression model, you calculate the R² score on test data and get 0.85. What does this value mean?

A The model explains 85% of the variance in the target variable on the test data.

B The model has 85% accuracy in classifying the test samples.

C The model's predictions are 85% similar to the training data.

D The model has an 85% chance of making a correct prediction.

🔧 Debug

expert

Debugging Unexpected Model Coefficients

You train a LinearRegression model on a dataset but get very large coefficient values that don't make sense. Which of the following is the most likely cause?

A The target variable contains categorical data instead of numeric values.

B The input features were not scaled or normalized, causing instability in coefficient values.

C The LinearRegression class does not support multiple features.

D The model was trained with fit_intercept=True, which always causes large coefficients.