Machine Learning Supervised Learning 2

Question 1

Which technique helps prevent overfitting by adding a penalty term proportional to the square of the model's coefficients?

Accepted Answer

L2 regularization

Answer

L2 regularization (Ridge) adds a penalty equal to the sum of squared coefficients, shrinking them toward zero without eliminating them.

Question 2

In a decision tree, what does 'pruning' accomplish?

Accepted Answer

Removes branches that provide little predictive power to reduce overfitting

Answer

Pruning removes overly specific branches from a decision tree to improve generalization on unseen data.

Question 3

What is the primary advantage of using an ensemble method like Random Forest over a single decision tree?

Accepted Answer

Lower variance through averaging multiple trees

Answer

Random Forest reduces variance by averaging predictions from many decorrelated trees built on random subsets of features and data.

Question 4

In the context of SVMs, what does the 'kernel trick' allow?

Accepted Answer

Mapping data into a higher-dimensional space without explicit computation

Answer

The kernel trick implicitly maps data to a higher-dimensional feature space using a kernel function, enabling linear separation of nonlinearly separable data.

Question 5

What does the learning rate hyperparameter control in gradient descent?

Accepted Answer

The size of steps taken toward the minimum of the loss function

Answer

The learning rate determines how large each parameter update step is; too large causes divergence, too small causes slow convergence.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Supervised Learning 2