CSCI 360: Introduction to Artificial Intelligence

In this lab you will be implementing Naive Bayes on a Breast Cancer data set. The algorithm uses a provided training set. You are expected to estimate posterior probabilities using training data.

In this lab you will be implementing the algorithm, but first you will have to clean the data. The data is found in the data.npy.

All the code you write should be in lab4.py and will be under functions preprocess_data, naive_bayesandcross_validation(optional). It is important you don't change the parameters. You are provided with a utility file and a test file. The utility file has functions provided that will compute the load_data.

It also contains the names of the features in the dataset in the order that they appear in the columns.

You are allowed to use numpy which is outlined by requirements.txt

The test file will try to use the preprocess_data, naive_bayesandcross_validation as they are outline in the lab4 PDF.

The test file uses load_data to pull a tuple from the data.