Chapter 2

Support code for Chapter 2: Learning How to Classify with Real-world Examples. The directory data contains the seeds dataset, originally downloaded from https://archive.ics.uci.edu/ml/datasets/seeds

chapter.py: The code as printed in the book.
figure1.py: Figure 1 in the book: all 2-by-2 scatter plots
figure2.py: Figure 2 in the book: threshold & decision area
figure4_5_sklearn.py: Figures 4 and 5 in the book: Knn decision borders before and after feature normalization. This also produces a version of the figure using 11 neighbors (not in the book), which shows that the result is smoother, not as sensitive to exact positions of each datapoint.
figure4_5_no_sklearn.py: Alternative code for Figures 4 and 5 without using scikit-learn
load.py: Code to load the seeds data
simple_threshold.py: Code from the book: finds the first partition, between Setosa and the other classes.
stump.py: Code from the book: finds the second partition, between Virginica and Versicolor.
threshold.py: Functional implementation of a threshold classifier
heldout.py: Evalute the threshold model on heldout data
seeds_knn_sklearn.py: Demonstrate cross-validation and feature normalization using scikit-learn
seeds_threshold.py: Test thresholding model on the seeds dataset (result mention in book, but no code)
seeds_knn_increasing_k.py: Test effect of increasing num_neighbors on accuracy.
knn.py: Implementation of K-Nearest neighbor without using scikit-learn.
seeds_knn.py: Demonstrate cross-validation (without scikit-learn)