Code for Regression and Classification

Python notebooks for all the computational examples about Regression and Classification from Chapter 12 of the book. See here for the full code repository.

• Premature mortality in US counties. Linear regression, ordinary least squares, coefficient of determination, explained variance
• Cartoon illustration of overfitting and generalization.
• Noise amplification in linear regression 1. Ordinary least squares, ridge regression
• Noise amplification in linear regression 2. Ordinary least squares, ridge regression
• Temperature in Versailles. Linear regression, ordinary least squares, ridge regression, sparse regression, lasso, regularization, overfitting
• Sparse regression 1. Sparsity, lasso, regularization
• Sparse regression 2. Sparsity, lasso, regularization
• Height and sex. Logistic regression, maximum likelihood, log-likelihood, logistic function
• Alzheimer’s diagnostics. Classification, logistic regression, evaluation of classification models, calibration
• Estimating wheat varieties via softmax regression.
• Digit classification. Softmax regression, overfitting, regularization
• Temperature estimation via regression trees.
• Temperature estimation via tree ensembles. Bagging, random forests, boosting, overfitting
• Log-likelihood of classification tree.
• Temperature estimation via neural networks. Regression, neural networks, deep learning, overfitting, early stopping
• Classifying wheat varieties via classification trees.
• Classifying wheat varieties via neural networks.