Assignment $3-2$: Regularized Logistic Regression

$1.$ Problem Description

Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests.

a$.$ From these two tests, you would like to determine whether the microchips should be accepted or rejected.

b$.$ To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.

$2.$ Dataset Introduction

File Assignment$3$data$2.$txt is the dataset. The first and second columns represent two different test results for the chip, and the third column represents whether the chip has passed quality assurance.

$3.$ Requirements

a$.$ Visualize dataset.

b$.$ Implement regularized logistic regression cost function and gradient.

c$.$ Draw decision boundaries.

d$.$ Calculate the accuracy of a regularized logistic regression model on the training set.

e$.$ How many and which parameters can be adjusted? State the values for results obtained in a~d$.$

f$.$ Choose at least one parameter to explore how its value affects the performance and try to optimize it$.$

data:$$Assignment$3$data$2.$txt