Top 10 Machine Learning Algorithms - Vocabulary Flashcards

Vocabulary flashcards covering the key machine learning algorithms described in the notes.

Linear Regression

A statistical method that predicts a continuous value by fitting a straight line (the regression line) to data; for example, Price = 100 * Size + 50,000.

Logistic Regression

A classification model that estimates the probability that an instance belongs to a class (e.g., spam vs not spam) and makes a decision based on a threshold (e.g., 0.8).

Decision Trees

A model that makes predictions by asking a sequence of questions (splitting data based on feature values) to reach a decision, such as predicting house price using size thresholds.

Random Forest

An ensemble method that combines predictions from multiple decision trees (usually by voting) to improve accuracy over a single tree.

Support Vector Machines (SVM)

A classifier that finds the optimal hyperplane that separates classes with maximum margin in feature space (e.g., separating expensive from affordable houses).

K-Nearest Neighbors (KNN)

An instance-based learning method that predicts a value or class by averaging the labels of the k closest data points in feature space (e.g., using the five nearest houses).

Naive Bayes

A probabilistic classifier based on Bayes' theorem with strong independence assumptions among features; often uses word frequencies to estimate probabilities (e.g., spam likelihood from words like 'free', 'money', 'discount').

K-Means Clustering

An unsupervised algorithm that groups data into k clusters by measuring similarity and assigning items to the nearest cluster center (e.g., neighborhood groups of houses).

Principal Component Analysis (PCA)

A dimensionality reduction technique that projects data onto a smaller set of principal components to preserve as much variance as possible (e.g., reducing many features to a single desirability or price score).

Neural Networks (Deep Learning)

Computational models with interconnected layers that learn to recognize complex patterns in data; they can analyze many features to predict outcomes (e.g., house prices) by learning relationships from large data.