random forest

If you are using Random Forest as your machine learning model, you don’t need to worry about scaling or normalizing your features. Random Forest is a tree-based model and hence does not require feature scaling. Tree-based models are invariant to the scale of the features, which makes them very user-friendly as this step can be skipped during preprocessing. Still, in practice you can see different results when you scale your features because of the way numerical values are represented in computers....

Interpreting and identifying crucial features in machine learning models can be a tough nut to crack, especially when dealing with black-box models. In this tutorial, we will dive deep into understanding global and local feature importance in Random Forests. We will explore several techniques and tools to analyze and interpret these importances, making our models more transparent and reliable. Let’s use the Red Wine Quality dataset from the UCI Machine Learning Repository to learn the techniques....