Generalized Linear Models

Linear Regression

Linear Regression is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is least squares, where we minimize the mean square error between the predicted values $\hat{y} = \textbf{X}\hat{\beta}$ and actual values $y$: $\left(y-\textbf{X}\beta\right)^{2}$.

We can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\hat{\beta}$.

Image Source: Wikipedia


Paper Code Results Date Stars


Task Papers Share
Time Series 20 8.40%
Federated Learning 10 4.20%
Meta-Learning 10 4.20%
General Classification 10 4.20%
Decision Making 9 3.78%
Feature Selection 8 3.36%
Imputation 6 2.52%
Variable Selection 6 2.52%
EEG 6 2.52%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign