Engines of AI: Algorithms and Metrics

These flashcards cover key concepts and metrics related to algorithms and the evaluation of machine learning models, focusing on linear and logistic regression.

Algorithm

A set of mathematical rules or a recipe that learns patterns from data.

Linear Regression

A regression algorithm used to predict continuous numerical values by fitting a best-fit line.

Logistic Regression

A classification algorithm that predicts discrete categories by outputting probabilities between 0 and 1.

Mean Absolute Error (MAE)

A metric that measures the average distance between predicted values and actual values.

Root Mean Squared Error (RMSE)

A common error metric that punishes larger errors more heavily by squaring them before averaging.

R-Squared (R²)

A measure of how well the regression model explains the variance of the target variable.

Confusion Matrix

A tool in classification that summarizes the performance of a model by comparing predicted and actual values.

True Positive (TP)

The scenario when the actual is positive and the prediction is also positive.

False Positive (FP)

The scenario when the actual is negative but the prediction is positive.

Precision

The ratio of true positives to the sum of true positives and false positives.

Recall

The ratio of true positives to the sum of true positives and false negatives.

F1-Score

The harmonic mean of precision and recall, used as a single metric for classification performance.