Logistic Regression & Decision Boundary
- Linear regression is not a good algorithm for classification problems (ie. binary classification with only 2 possible options - yes/no).
- Decision boundary
- Logistic regression : Logistic regression is a statistical model that uses the logistic function, or logit function, in mathematics as the equation between x and y. The logit function maps y as a sigmoid function of x.
- Outputs between $\textcolor{red}{0}$ and $\textcolor{red}{1}$
- If you plot this logistic regression equation, you will get an S-curve as shown below:
Definition of the logistic function:
\[\sigma : \mathbb{R} \rightarrow (0,1)\] \[\sigma(z) = \frac{1} {1 + e^{-z}}\]Decision boundary
Usually if >=0.5 it is considered as 1 , else 0.
Out of the scope ref’s
📝 | Sitenin son güncellenme tarihi: 08 Kas 2024