I have trained 3,600 Random Forest Classifiers (each with 1,000 trees) on 72 data sets (from OpenML-CC18 benchmark) to check how many trees should be used in the Random Forest. What I’ve found:

The Random Forest is an esemble of Decision Trees. A single Decision Tree can be easily visualized in several different ways. In this post I will show you, how to visualize a Decision Tree from the Random Forest.

The feature importance describes which features are relevant. It can help with better understanding of the solved problem and sometimes lead to model improvement by utilizing the feature selection. In this post, I will present 3 ways (with code) to compute feature importance for the Random Forest algorithm from scikit-learn package (in Python).

In this post I will show you how to save and load Random Forest model trained with scikit-learn in Python. The method presented here can be applied to any algorithm from sckit-learn (this is amazing about scikit-learn!).

The Random Forest algorithm from scikit-learn package can sometimes consume too much memory:

Which is better: Random Forest or Neural Network? This is a common question, with a very easy answer: it depends :) I will try to show you when it is good to use Random Forest and when to use Neural Network.

Random Forest versus AutoML you say. Hmmm…, it’s obvious that the performance of AutoML will be better. You will check many models and then ensemble them. This is true, but I would like to show you other advantages of AutoML, that will help you deal with dirty, real-life data.

When I first saw this question I was a little surprised. The first thought is, of course, they do! Any complex machine learning algorithm can overfit. I’ve trained hundreds of Random Forest (RF) models and many times observed they overfit. The second thought, wait, why people are asking such a question? Let’s dig more and do some research. After quick googling, I’ve found the following paragraph on Leo Breiman (the creator of the Random Forest algorithm) website: