Plot bias, variance and overall accuracy for a boosted ID3 decision tree on the SPECT Heart dataset.
This implements a multi-class ID3 decision tree using Information Gain as the fit function and a Chi-square test and/or max tree depth to determine when to prune. See Induction of Decision Trees, Ross Quinlan, 1986 for details.
dtree_test.py initially trains the full training set then runs a test and prints the accuracy. It then goes on to train using 25 bootstrap samples (bagging), repeatedly for tree depths from 1 to 10.
The bias, variance and overall accuracy is then plotted against tree depth.
Accuracy on the small dataset provided is about 71% which seems fairly reasonable. The accuracy had been at a little over 60% before @surajrautela pointed out an error in the Information Gain calculation.