Author(s): Shenggang Li

Originally published on Towards AI.

Unlocking the Secrets of Decision Trees: From Basic Concepts to Advanced Optimization Techniques and Practical Coding
Photo by Natalie Thornley on Unsplash

This post explores decision trees and guides how to build one from scratch. I’ll start with the basics and gradually move on to more advanced techniques to enhance the decision tree.

I’ll begin with a simple example to explain the basics of decision trees. Then, I’ll delve into the mathematics behind them, covering key concepts like entropy and Gini impurity. I’ll also introduce the soft trees using the logistic function.

After covering the theory, I’ll dive into coding to show you how to build your decision tree, without using pre-built libraries. Finally, I’ll explore advanced techniques to optimize the tree’s performance, such as using KS statistics and combining different metrics.

By the end of this guide, you’ll have a solid understanding of decision trees and the confidence to build and tweak your AI models. Let’s get started!

Let’s dive into the basics of decision trees using a simple example. Imagine we have data from 1000 individuals with different ages (our input variable x), and we want to predict whether they are employed ( target variable Y, binary: 1 for employed, 0 for not employed). The goal is to build a model f(x)=Y that predicts employment status.

To start, we need… Read the full blog for free on Medium.

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Published via Towards AI