This supervised learning mcq tests your grasp on key machine learning domains such as regression, classification, and metrics.

Online Supervised Learning MCQ

What type of algorithm is K-Nearest Neighbors (KNN)?

Model performs well on both train and test data

Model performs well on training but poorly on test data

What is overfitting in supervised learning?

What is a decision tree commonly used for?

Which of the following is a classification algorithm?

Classification predicts continuous values

Regression predicts continuous values, classification predicts categories

What is the key difference between regression and classification?

Which of the following is a hyperparameter in model training?

What is the purpose of using cross-validation in supervised learning?

Which metric is commonly used to evaluate regression models?

True Positives / (True Positives + False Positives)

True Positives / (True Positives + False Negatives)

How is Precision defined in classification problems?

It penalizes large weights to prevent overfitting

What is the purpose of regularization in supervised learning?

To reduce overfitting and validate model performance

By learning from residual errors of previous models

How does Gradient Boosting improve performance?

Maintains class distribution in training and test sets

What is the advantage of using stratified sampling in supervised learning?

Balance between underfitting and overfitting

What does the bias-variance tradeoff help address in machine learning?