Machine Learning Supervised Learning 3

Question 1

In k-Nearest Neighbors (k-NN), what is the effect of choosing a very large value of k?

Accepted Answer

Smooths the decision boundary, potentially underfitting

Answer

A large k averages over many neighbors, creating a smoother but potentially underfit decision boundary that may miss local patterns.

Question 2

Which of the following best describes 'stratified k-fold cross-validation'?

Accepted Answer

Each fold preserves the same class distribution as the full dataset

Answer

Stratified k-fold ensures each fold has roughly the same proportion of class labels as the original dataset, important for imbalanced data.

Question 3

What is the purpose of a validation set in a supervised learning workflow?

Accepted Answer

To tune hyperparameters without contaminating the test set

Answer

A validation set is used during training to tune hyperparameters and make model selection decisions, keeping the test set pristine for final evaluation.

Question 4

In logistic regression, what function maps the linear output to a probability between 0 and 1?

Accepted Answer

Sigmoid

Answer

The sigmoid function squashes any real-valued number to the range (0, 1), making it suitable for binary probability estimation.

Question 5

Which phenomenon occurs when a model learns noise and specific patterns of the training data that do not generalize to new data?

Accepted Answer

Overfitting

Answer

Overfitting happens when a model is too complex and memorizes training data details, resulting in high training accuracy but poor test accuracy.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Supervised Learning 3