Build Your First Decision Tree in Python with scikit-learn

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Are you intrigued by the power of decision-making in machine learning?

By the end of this tutorial, you'll have a solid grasp of Decision Trees, be capable of implementing them in Python, and understand their role in various machine learning projects.

What you'll discover:

The fundamentals of Decision Trees: How they make decisions and create splits
Hands-on coding: Building Decision Trees in Python using popular libraries
Pruning and preventing overfitting: Strategies for optimizing Decision Tree performance

Code: https://ryanandmattdatascience.com/decision-tree/

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🍿 WATCH NEXT
Scikit-Learn and Machine Learning Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4LNmObS0gqNVyNdVfXnHwu8
KNN Classification: https://youtu.be/Nz73vXn5afE
Logistic Regression: https://youtu.be/aL21Y-u0SRs
Support Vector Machine: https://youtu.be/kPkwf1x7zpU

In this video, I show you how to build a decision tree machine learning algorithm using sklearn and Python. Decision trees are supervised machine learning models that use pre-labeled data and split information based on different criteria, similar to how a flowchart works. We walk through the entire process, from understanding the structure of root nodes, decision nodes, and leaf nodes, to coding a complete example using baseball statistics.

I use real data from the top 500 MLB hitters to predict Hall of Fame inductions, demonstrating how to import data with pandas, clean and prepare features, split data into training and testing sets, and implement the DecisionTreeClassifier. We explore key metrics like confusion matrices, precision, recall, and F1 scores to evaluate model performance. I also show you how to identify feature importance and optimize your model using parameters like criterion and ccp_alpha to prevent overfitting.

While decision trees may not be the most accurate model available, they are incredibly simple to code and quick to run, making them an excellent starting point for anyone learning machine learning. The complete code and dataset are available on my GitHub, linked in the description below. If you found this tutorial helpful, make sure to subscribe for more machine learning content!

TIMESTAMPS
00:00 Introduction to Decision Trees
01:05 Setting Up & Importing Data
02:11 Data Cleaning & Preparation
03:02 Splitting Data (X and Y)
03:55 Train Test Split
05:05 Decision Tree Classifier
06:42 Fitting & Making Predictions
07:22 Confusion Matrix
08:17 Classification Report
09:00 Feature Importances
10:42 Building Features DataFrame
11:30 Second Model with Parameters
13:00 Comparing Model Results
14:13 CCP Alpha Impact on Features

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Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.

Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.

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