#2020-07-18 12:43:12

Practical, powerful, efficient and versatile. No wonder why Random Forests are so popular and heavily utilized.

#SUPERVISED

#MACHINE LEARNING

#CLASSIFICATION

#REGRESSION

Random Forests

  • Data Size: 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: 80%

Random forest tutorials in python

1- Simple Implementation

Don't wait 10 years before you progress towards your data science goals. Here is a quick and dirty Random Forest implementation that everyone can understand quickly.

2- Step by Step

This is a step by step explanation of basic scikit-learn implementations of Random Forests. The steps are simplified to be very easy to understand and grasp the basics immediately.

3- Optimization

Learn about important parameters when working with random forest algorithms and how to optimize them.

4- Pros & Cons

Understand the Pros & Cons of Random Forest Machine Learning applications. This knowledge can define the difference between a data science rookie and a machine learning expert.

5- History

Read about the history of Random Forest algorithm.