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Supervised learning is a type of machine learning where the model is trained on labeled data, meaning each training example includes an input and its corresponding correct output. The algorithm learns a mapping from inputs to outputs, which it then uses to make predictions on unseen data. Common tasks include classification and regression.
Unsupervised learning is a type of machine learning where the model is trained on unlabeled data and must discover hidden patterns or structures on its own. Common tasks include clustering (grouping similar data points), dimensionality reduction, and anomaly detection. Examples include k-means clustering and PCA.
Classification predicts a discrete label (e.g., spam or not spam), while regression predicts a continuous value (e.g., house price). Classification outputs categories, whereas regression outputs numerical quantities. Both are supervised learning tasks.
Linear regression is a supervised learning algorithm that models the relationship between a dependent variable and one or more independent variables by fitting a straight line (or hyperplane). The model minimizes the sum of squared residuals between predicted and actual values. In scikit-learn, it is implemented as LinearRegression().
Logistic regression is a supervised learning algorithm used for binary classification despite its name containing "regression." It uses the sigmoid function to map predictions to probabilities between 0 and 1. A threshold (typically 0.5) is applied to convert probabilities into class labels.