Feature Importance Analysis in Machine Learning

<blockquote> Hello, everyone! My name is Anar. I am a machine learning engineer and, as a side gig, a co-owner of SPUNCH, where I have the honor of exploring new horizons in Data Science :) In this series of articles, I will focus on real-life cases from the business world that have helped me and, I hope, will help you. </blockquote> Alright, let’s get started! Today, I want to talk about the rather intricate process of feature importance analysis after obtaining model results and a tool for working with it. This process is crucial when you need to: <ol> <li>Improve the model’s quality.</li> <li>Interpret the contribution of features to the model.</li> <li>Visualize the impact of features on the target variable.</li> <li>Present the obtained results to management or clients.</li> </ol> For beginners, feature analysis is extremely useful to avoid including multicollinear features in the model. This will allow the model to work faster and, consequently, provide faster inferences. Permutation Importance <blockquote> In simple terms, feature importance analysis involves shuffling the values within each feature, which means instead of having values like b21, b22, you’ll have values like b24, b29. In this case, the distribution of the feature remains the same, but the relationship between feature values and classes changes. If the prediction quality significantly deteriorates after such a change to a feature, it can be concluded that the feature is highly important. </blockquote> Let’s dive into the code: <ol> <li>We will create synthetic data for our binary classification task with two features and two classes: 0 and 1. Here’s some Python code to generate this synthetic data:</li> </ol> <a href="https://medium.com/@SPUNCH/feature-importance-analysis-in-machine-learning-e0b5caf80ffc">Read More </a>