Regression on English AI Terms Dictionary

Spike-and-slab regression

Sat, 18 Jul 2026 10:16:41 +0000

Definition

Spike-and-slab regression is a Bayesian statistical technique used for variable selection and sparse modeling. It employs a mixture prior distribution consisting of two components: a ‘spike’ (typically a narrow distribution centered at zero) representing null effects, and a ‘slab’ (a broader distribution) representing significant effects. This approach allows the model to automatically determine which predictors are relevant by shrinking irrelevant coefficients toward zero while retaining large estimates for important ones, effectively performing feature selection within a probabilistic framework.

Proximal gradient methods for learning

Sat, 18 Jul 2026 10:12:36 +0000

Definition

Proximal gradient methods are iterative optimization techniques used when the loss function includes a differentiable smooth term and a non-differentiable regularizer, such as L1 norm. The algorithm combines gradient descent steps on the smooth part with a proximal operator that handles the non-smooth part. This makes them particularly useful for sparse learning and regularization tasks where traditional gradient descent fails due to non-differentiability.

Summary

Optimization algorithms designed to minimize composite objective functions containing both smooth and non-smooth components.

Multivariate adaptive regression spline

Sat, 18 Jul 2026 10:08:53 +0000

Definition

Multivariate Adaptive Regression Splines (MARS) is a flexible regression method that models complex nonlinear relationships by fitting piecewise linear basis functions. It automatically selects the location and direction of the knots in the data, allowing it to adapt to local variations without requiring manual specification of the functional form. MARS is particularly effective for high-dimensional data and handles interactions between variables naturally, making it a powerful tool for predictive modeling in various scientific and engineering domains.