This document discusses linear models for classification. It outlines an agenda covering logistic regression, its limitations for multi-class classification problems and predicting unstable boundaries with limited data. It also mentions the need for linear discriminant analysis and addressing bias-variance tradeoffs, errors, and multicollinearity which can impact models. The document provides context and an overview of key topics for working with linear classification models.

1. zekeLabs
Linear Models for Classification

2. “Goal - Become a Data Scientist”
“A Dream becomes a Goal when action is taken towards its achievement” - Bo Bennett
“The Plan”
“A Goal without a Plan is just a wish”

3. ● Logistic Regression
● Need for Linear Discriminant Analysis
● Bias-Variance Tradeoff
● Correcting Bias-Variance Error
● Reducing Error using Learning algorithm
● Multicollinearity
● Impact of multicollinearity
● Detecting multicollinearity
● Correcting multicollinearity
Agenda

4. Logistic Regression - Limitations
● Logistic Regression is meant for binary classification & not often used for
multi-class classification
● Predicts unstable decision boundary when classes are well separated
● Unstable predictor with less training data

6. Understanding Variance
https://www.listendata.com/2014/11/detecting-and-correcting.html