Week 39: Resampling methods and logistic regression
Contents
Plan for week 39, September 22-26, 2025
Readings and Videos, resampling methods
Readings and Videos, logistic regression
Lab sessions week 39
Lecture material
Resampling methods
Resampling approaches can be computationally expensive
Why resampling methods ?
Statistical analysis
Resampling methods
Resampling methods: Bootstrap
The bias-variance tradeoff
A way to Read the Bias-Variance Tradeoff
Understanding what happens
Summing up
Another Example from Scikit-Learn's Repository
Various steps in cross-validation
Cross-validation in brief
Code Example for Cross-validation and \( k \)-fold Cross-validation
More examples on bootstrap and cross-validation and errors
The same example but now with cross-validation
Logistic Regression
Classification problems
Optimization and Deep learning
Basics
Linear classifier
Some selected properties
Simple example
Plotting the mean value for each group
The logistic function
Examples of likelihood functions used in logistic regression and nueral networks
Two parameters
Maximum likelihood
The cost function rewritten
Minimizing the cross entropy
A more compact expression
Extending to more predictors
Including more classes
More classes
Optimization, the central part of any Machine Learning algortithm
Revisiting our Logistic Regression case
The equations to solve
Solving using Newton-Raphson's method
Example code for Logistic Regression
Synthetic data generation
Readings and Videos, logistic regression
Hastie et al 4.1, 4.2 and 4.3 on logistic regression
Raschka et al, pages 53-76 on Logistic regression and pages 37-52 on gradient optimization
Video on Logistic regression
Yet another video on logistic regression
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