Skip to content

ahester57/LogisticRegression

Repository files navigation

Logistic Regression

Austin Hester

CS 4340 - Intro to Machine Learning

Uday Chakraborty

Uses logistic regression to formulate a correlation between weeks of inactivity and probability of passing the course.

Our data:
X | Y
1 | 0
2 | 1
3 | 0
4 | 1
5 | 0
6 | 1
7 | 1
8 | 1,
where X is weeks of inactivity and Y is pass/fail (0/1)

We use logistic to regression to find a weight vector.
With a step size, c = 0.01, weights initialized at 1, and 2000 iterations,
we obtain w_v = { -1.81 , 0.55 }.

We can use these weights to find the chance of passing given weeks of inactity.

P ( Y(j) = 0 | X(j) ) = 1 / (1 + e^(-1.81 + (X(j) x 0.55)))

At x = 3, we get

P ( Y = 0 | x = 3 ) = 1 / (1 + e^(-1.81 + (3 x 0.55))) = 0.535

Which means, at 3 weeks of inactivity, you have a 53.5% chance of passing the course.

About

(Python) Logistic Regression Exercise for Intro to Machine Learning

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published