ENGR 421 / DASC 521 Homework 02-naive Bayes’ Classifier Solution

In this homework, you will implement a naïve Bayes’ classifier in Python. Here are the steps you need to follow:
1. Read Section 5.7 from the textbook.

2. You are given a multivariate classification data set, which contains 35000 clothing images of size 28 pixels × 28 pixels (i.e., 784 pixels). These images are from five distinct classes, namely, T-shirt, Dress, Coat, Shirt, and Bag. The figure below shows five sample clothing images from each class. You are given two data files:
a. hw02_images.csv: clothing images,
b. hw02_labels.csv: corresponding image labels (1: T-shirt, 2: Dress, 3: Coat, 4: Shirt, 5: Bag).



3. Divide the data set into two parts by assigning the first 30000 images to the training set and the remaining 5000 images to the test set.



print(sample_means)
[[254.99866667 254.98416667 254.85616667 ... 254.679 254.87816667 254.95933333]
[254.99733333 254.99733333 254.9965 ... 254.96883333 254.99216667 254.98866667]
[254.99933333 254.99933333 254.99233333 ... 251.52483333 254.4725
254.97483333]
[254.99666667 254.98983333 254.91416667 ... 252.39516667 254.44166667
254.93666667]
[254.999 254.98433333 254.93783333 ... 250.673 253.23333333 254.79083333]]

print(sample_deviations)
[[ 0.09127736 0.25609108 1.31090756 ... 5.29826629 3.9117332 1.93959091]
[ 0.2065419 0.2065419 0.2163818 ... 1.04076669 0.47057267 0.70062226]
[ 0.05163547 0.04081939 0.16002465 ... 18.43665868 6.7881694 1.1061344 ]
[ 0.18436076 0.21617116 1.81046936 ... 15.67799977 6.34549162 1.79971911]
[ 0.04471018 0.64582342 3.03248555 ... 23.62576428 13.9167006 4.4727787 ]]
print(class_priors) [0.2 0.2 0.2 0.2 0.2]


5. Calculate the confusion matrix for the data points in your training set using the parametric classification rule you will develop using the estimated parameters. Your confusion matrix should be the following matrix.

y_truth 1 2 3 4 5 y_pred
1 3685 49 4 679 6
2 1430 5667 1140 1380 532
3 508 208 4670 2948 893
4 234 60 123 687 180 5 143 16 63 306 4389

6. Calculate the confusion matrix for the data points in your test set using the parametric classification rule you will develop using the estimated parameters. Your confusion matrix should be the following matrix.

y_truth 1 2 3 4 5 y_pred
1 597 6 0 114 1
2 237 955 188 267 81
3 92 25 785 462 167
4 34 11 16 109 29
5 40 3 11 48 722
What to submit: You need to submit your source code in a single file (.py file) and a short report explaining your approach (.doc, .docx, or .pdf file). You will put these two files in a single zip file named as STUDENTID.zip, where STUDENTID should be replaced with your 7-digit student number.
How to submit: Submit the zip file you created to Blackboard. Please follow the exact style mentioned and do not send a zip file named as STUDENTID.zip. Submissions that do not follow these guidelines will not be graded.
Cheating policy: Very similar submissions will not be graded.