[Linear Classification] From Linear Regression to Linear Classification
[Linear Classification] From Linear Regression to Linear Classification

In the posts 'Introduction to Linear Regression', 'Simple Linear Regression' and 'Polynomial Regression and Features-Extension of Linear Regression', we had discussed the regression task. The goal of regression is to find out a function or hypothesis that given an input $\boldsymbol{x}$, the hypothesis can make a prediction $\hat{y}$ which should be as close to the target $y$ as possible.

[Linear Regression] Maximum Likelihood Estimation
[Linear Regression] Maximum Likelihood Estimation

To any input $\boldsymbol{x}$, our goal in a regression task is to give a prediction $\hat{y}=y(\boldsymbol{x})$ to approximate target $t$ where the function $y$ is the chosen hypothesis. And the difference between $t$ and $\hat{y}$ can be called 'error' or more precisely 'loss'.