Logistic Regression:From Scratch

Logistic Regression Behind the Scenes.

y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

  • Logit = ln(P(X)/(1-P(X)))
  • ODDS = P(X)/(1-P(X)
  • The output variables must always be binary because Logistic Regression assumes output as Binary.
  • Logistic Regression assumes there exist no noise ,so remove all the noise from data.
  • Logistic Regression assumes Gaussian Distribution and there exist a linear relationship between our input and output variable.
  • Remove all the correlated input variables. If correlated input variables occur model can highly overfit.




Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Introduction to Movie Recommendation System for Beginners

Part 8: Review of Logistic Regression and its Implementation in Tensorflow / Keras

Smart rankings at Farfetch: 101

Github Network Science 2: Loading JanusGraph

How diversity could solve for algorithmic bias

How to Build a Smart Vision-Based Sudoku Solver

Creating new “art” works by integrating Creative Adversarial Network (CAN) and Neural Style…

NLP Pipeline Step-by-Step

pipelines in a process

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Akshar Rastogi

Akshar Rastogi

More from Medium

Understanding Decision Trees ID3 Algorithm |Machine Learning

Association rule mining fundamentals

A take on implementing Linear Regression from scratch — Part 1

Random Forests: Week 7