ISLR - An Introduction to Statistical Learning

0.05MB. 0 audio & 1 images. Updated 2020-07-24.

Description

An Anki deck for the content of the book An Introduction to Statistical Learning with Applications in R (7th edition) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. This deck is not meant to replace reading the book. Rather, it is supposed to help repeating the main terms and core content of the chapters. It does not cover the conceptual and practical exercises from the book, which are typically longer, more detailed and require deeper thinking. I have created cards for elements that I considered relevant, but this is certainly a judgment call. Features

200 cards
covering all 10 chapters
each card contains a note with the corresponding page in the book (7th edition) for further reading if required
each card is tagged with the corresponding chapter number, allowing to focus on specific chapters or to use the deck while reading along

Sample (from 182 notes)

Cards are customizable! When this deck is imported into the desktop program, cards will appear as the deck author has made them. If you'd like to customize what appears on the front and back of a card, you can do so by clicking the Edit button, and then clicking the Cards button.

Front	When making predictions based on a model, confidence intervals account for the reducible error in the model, but not for the irreducible error in the model.True or False?
Back	True.
Notes	p. 81
Tags	chapter3

Front	Describe the main element of bagging / bootstrapped aggregation for decision trees.
Back	1) Create several bootstrapped training sets2) Fit a separate decision tree on each bootstrapped training sets(low bias, but high variance of those separate models)3) Average over the separate models to get a model with lower variance
Notes	p. 316-317
Tags	chapter8

Front	Backward stepwise model selection: Describe the main process.
Back	1. Start with a model that includes all predictors.2. Compute all models with one fewer predictor and select the model which yields the smallest residual sum of squares. (This identifies the predictor with the least additional value.)3. Continue the second step until we have a model without any predictors.4. Select the best overall model based on cross-validation prediction error, AIC/BIC or similar measures.
Notes	p. 209
Tags	chapter6

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Cool, that's what I've expected to find!