arrow_back
Section 1: Logistic Regression - Level 1
Introduction to Logistic Regression
Download Resources
Motivation behind Logistic Regression
Logistic Regression Theory 1 - Sigmoid
Logistic Regression Theory 2 - Log Odds
Log Odds vs Sigmoid Squashing
Section 2: Logistic Regression Theory - Level 2
Maximum Likelihood Estimation - Part 1
Maximum Likelihood Estimation - Part 2
Logistic Regression via Gradient Descent
Multi Class Logistic Regression - One vs Rest
Course Review
Section 3: Logistic Regression Concepts - Level 3
Regularized Logistic Regression
Multicollinearity - How it affects inference
Perturbation Technique
Logistic Regression for Non Linearly Separable Data
Feature Engineering Approaches
Section 4: Examining Model Fit (Project)
Problem Statement
EDA
Building Logistic Regression
Perturbation Test Code Demo
Scikit vs Statsmodels Logit API
McFaddens Pseudo R-Squared
Likelihood Ratio Test
Walds Statistic
Section 5: Evaluating Performance
Predicting on Test Data
Accuracy and caveats
Confusion Matrix, TPR, FPR, TNR, FNR
Precision and Recall with F1 Score
Receiver Operating Characteristic (ROC) Curve
Concordance and Discordance
Cross Entropy
KS Statistic and Gains Table
Hyper Parameter Tuning
Evaluation Metrics Demo
Review Questions
Assignment