#2020-07-18 11:39:13

More geared towards classification Logistic Regression is still a Linear Model that’s commonly used today.

Basically, it’s very old, usually accurate, super scalable and it also produces statistical probability outputs.

Just like linear regression its biggest limitation probably comes out when you have a non-linear dataset.

#SUPERVISED

#MACHINE LEARNING

#CLASSIFICATION

#REGRESSION

Logistic Regression

  • Dataset: Large and Small
  • Speed: Fair
  • Ease of Use: Normal
  • Normalization: No
  • Predictor: Numeric or Categorical
  • Primary Problem: Multiclass or Binary
  • Mixed-type: Yes
  • Missing Data Handling: Yes
  • Popularity: 75%

LOGISTIC REGRESSION tutorials in python

1- Simple Implementation

Most basic and straightforward implementation of Logistic Regression. Machine Learning demonstrations simplified for everyone who knows basic Python.

2- Step by Step

Easy to follow step-by-step breakdown of Logistic Regression Machine Learning implementations.

3- Optimization

Learn about important parameters when working with Logistic Regression Algorithms and how to optimize them.

4- Pros & Cons

Pros and Cons of Logistic Regression. Learn when it's advantageous to use them and when you should steer away from them.

5- History

Read about the history of Logistic Regression Algorithm.