Machine Learning Supervised Learning 4

Question 1

Which loss function is typically used for training a binary classification model with logistic regression?

Accepted Answer

Binary Cross-Entropy

Answer

Binary cross-entropy (log loss) measures the divergence between predicted probabilities and true binary labels, making it ideal for logistic regression.

Question 2

What is the main difference between bagging and boosting ensemble methods?

Accepted Answer

Bagging trains models in parallel on random subsets; boosting trains sequentially focusing on errors

Answer

Bagging (e.g., Random Forest) trains models in parallel on bootstrap samples to reduce variance, while boosting trains models sequentially to reduce bias by focusing on previous errors.

Question 3

In a confusion matrix for binary classification, what does a 'False Positive' represent?

Accepted Answer

A negative instance incorrectly classified as positive

Answer

A false positive (Type I error) occurs when the model predicts positive but the true label is negative.

Question 4

Which regularization method can perform automatic feature selection by driving some coefficients exactly to zero?

Accepted Answer

L1 (Lasso)

Answer

L1 (Lasso) regularization uses the absolute value of coefficients in its penalty, which tends to produce sparse solutions with some weights exactly zero.

Question 5

What is 'feature scaling' and why is it important for algorithms like SVMs and k-NN?

Accepted Answer

Normalizing feature ranges so no single feature dominates distance calculations

Answer

Feature scaling (e.g., standardization or min-max normalization) ensures all features contribute equally to distance-based calculations, which is critical for SVMs and k-NN.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Supervised Learning 4