WST 212 - Supervised Learning Flashcards

Flashcards covering key concepts from the Supervised Learning lecture.

What is supervised learning?

A type of machine learning where models are trained on labelled data.

What is unsupervised learning?

A type of machine learning that deals with unlabelled data.

What are some common supervised learning algorithms?

Linear regression, logistic regression, naive Bayes, decision trees.

What are some common unsupervised learning algorithms?

K-means clustering, hierarchical clustering, principal component analysis (PCA).

What is an example of supervised learning?

Apple’s Face ID system.

What is an example of unsupervised learning?

The TikTok "For You Page" algorithm.

What does 'x' represent in simple linear regression?

Independent variable.

What does β₀ represent in simple linear regression?

The intercept of the line.

What does β₁ represent in simple linear regression?

The slope of the line.

What is the main difference between logistic and linear regression?

Predicting probabilities whilst linear regression predicts continuous values.

What is the output range of the sigmoid (or logistic) function used in logistic regression?

0 to 1

What is the purpose of Training Data in model validation?

To train the chosen model by feeding it input features and corresponding labels to learn the relationship between them.

What is the purpose of Test Data in model validation?

Assess the model's performance.

What are real world examples of binary classification?

Spam detection, Credit card fraud, Health, Marketing, Banking.