The point of this Lab is to get you working with Naive Bayesian Classifiers and to continue on the testing mindset.

To get your environment running you will need Python 3.5.x (I've tested both). You will also have to install some packages:

pip install bs4 chardet 

To run this experiment I want you to work with crossvalidate.py and naive_bayes/spam_trainer.rb

The goal of this Lab is to get you looking at the nuances of the naive bayesian classifier.

Note that probabilities tend to be somewhat useless on naive bayesian classifiers so we have left out ROC curves here.

Shortening the fold1.label and fold2.label files will help increase the speed of crossvalidating.

Cross Validation Parsing emails for ./tests/fixtures/fold2.label Done parsing files for ./tests/fixtures/fold2.label Predicted SPAM | HAM SPAM 290.0 | 333.0 HAM 2.0 | 1400.0 ----------------- Precision: 0.9931506849315068 Recall: 0.4654895666131621 Accuracy: 0.8345679012345679 Parsing emails for ./tests/fixtures/fold1.label Done parsing files for ./tests/fixtures/fold1.label Predicted SPAM | HAM SPAM 273.0 | 347.0 HAM 3.0 | 1389.0 ----------------- Precision: 0.9891304347826086 Recall: 0.4403225806451613 Accuracy: 0.8260437375745527