Statistics
β Regression
Predicting continuous outcomes — from the simplest straight line to regularized models that generalize.
4 concepts— start at the top and work your way down
- 1→
Linear Regression
Fitting a straight line to data to model and predict the relationship between two variables.
- 2→
Logistic Regression
Modelling the probability of a binary outcome using the sigmoid function — fitting by maximum likelihood or gradient descent.
- 3→
Regularization
Adding a penalty on model complexity to prevent overfitting — L1 (Lasso) induces sparsity, L2 (Ridge) shrinks coefficients smoothly.
- 4→
Generalized Linear Models
A unifying framework — link function plus exponential-family distribution — that includes linear, logistic, and Poisson regression as special cases.