How I obtained the probability of a prediction using Jax’s logistic regression model

One thing that I have done to learn Python’s Jax library is to study code and also study the Stackoverflow website because it contains a wealth of knowledge concerning programming tips. For instance, I read on a Stackoverflow post that the predict_proba attribute is actually a regression problem and not a classification one.

Kaggle’s playground competition season 4 episode 1 is a competition where the probability that a customer will churn is predicted on. I used a linear regression model to solve the problem and it can be read here:- https://medium.com/@tracyrenee61/how-i-won-my-45th-bronze-medal-on-kaggles-playground-competition-season-4-episode-1-5b0b87504b2b

A data scientist must continually study code and other theoretical topics to stay abreast of the field, so I carried on studying the logistic regression model to see what additional information that I could glean from it.

Logistic regression is a statistical model often used for classification and predictive analytics. Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given dataset of independent variables. Since the outcome is a probability, the dependent variable is bounded between 0 and 1. In logistic regression, a logit…