Naïve Bayes Classification Model
Background:
Bayesian is one of the simplest probabilistic classifier that classifies a candidate test vector based on Bayes Rule:
Lab Tasks
Task 1
Develop a python program to implement Bayesian classification model for the following dataset and classify the given test vector:
|
Age |
Loan |
Class |
|
25 |
40000 |
0 |
|
35 |
60000 |
0 |
|
45 |
80000 |
0 |
|
20 |
20000 |
0 |
|
35 |
120000 |
0 |
|
52 |
18000 |
0 |
|
23 |
95000 |
1 |
|
40 |
62000 |
1 |
|
60 |
100000 |
1 |
|
48 |
220000 |
1 |
|
33 |
150000 |
1 |
|
48 |
142000 |
? |
Task 2
Use the given cancer dataset and classify it using Bayesian classification model:
a) First create a python script and load
‘cancer’ file.
b) Identify features and classes from the
loaded dataset.
c) Perform 2-fold cross validation on the
dataset by splitting it into testing and training parts.
d) Implement a Bayesian classifier using the
above algorithm and use training dataset to classify each of the sample within
testing dataset.
e) Compute the accuracy from the predicted
test samples.