Facing a Classification Project in Machine Learning - WebSystemer.no

After modeling, the next stage is always analyzing how our model is performing and why it is doing what it's doing. However, if you've had the chance to work with ensemble methods, you probably already know that these algorithms are usually known as "black-box models". These models lack explicability and interpretability since the way they usually work implies one or several layers of a machine making decisions without human supervision, apart from a group of rules or parameters set. More often than not, not even the most expert professionals in the field can understand the function that is actually created by, for example, training a neural network. In this sense, some of the most classical machine learning models were actually better. That's why, for the sake of this post, we'll be analyzing the feature importance of our project using a classic Logistic Regression.