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 $\hat{\beta}$.
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regression | 232 | 36.36% |
In-Context Learning | 33 | 5.17% |
Prediction | 20 | 3.13% |
Continual Learning | 11 | 1.72% |
Causal Inference | 11 | 1.72% |
Uncertainty Quantification | 9 | 1.41% |
Decision Making | 9 | 1.41% |
Federated Learning | 8 | 1.25% |
Computational Efficiency | 8 | 1.25% |
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