A custom implementation of a Naive Bayes Classifier written from scratch in Python 3.
From Wikipedia:
In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Home Owner | Marital Status | Annual Income | Defaulted Borrower |
---|---|---|---|
Yes | Single | $125,000 | No |
No | Married | $100,000 | No |
No | Single | $70,000 | No |
Yes | Married | $120,000 | No |
No | Divorced | $95,000 | Yes |
No | Married | $60,000 | No |
Yes | Divorced | $220,000 | No |
No | Single | $85,000 | Yes |
No | Married | $75,000 | No |
No | Single | $90,000 | Yes |
Source: Introduction to Data Mining (1st Edition) by Pang-Ning Tan
Figure 5.9, Page 230
Please run with Python 3 or greater.
python main