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
BIG-bench Machine Learning 45 14.61%
Time Series 23 7.47%
Federated Learning 18 5.84%
Management 10 3.25%
Generalization Bounds 8 2.60%
Variable Selection 7 2.27%
Meta-Learning 7 2.27%
Learning Theory 7 2.27%
Decision Making 7 2.27%


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