Streamlining Feature Selection: Statistical Approach with ML in Python using Optimal Binning and Logistic Regression
Introduction
In machine learning, making sure you have the right features is really important. It’s like picking the best ingredients for a recipe. The scorecard method we’re talking about helps us pick these important features. It looks at the data in a smart way and uses a combination of two techniques: optimal binning and logistic regression. This helps us create a scoring system that predicts outcomes accurately and in a way that’s easy to understand.
Let’s say you’re trying to predict heart disease. You might have features like age, sex, cholesterol levels, and whether the person has high blood sugar. The scorecard method looks at these features and decides which ones are most important for predicting heart disease.
In this article, we’ll show you how to use the scorecard method to pick the best features for your machine learning model.
Step 1: Data Preparation
We’ll start by preparing our data. For this example, we’ll use a sample dataset containing several features (age, sex, cp, trestbps, chol, fbs) and a binary target variable target.
