Gradient Boosting in Scikit-Learn: Hands-On Tutorial

Published: 28 September 2023
on channel: Ryan & Matt Data Science

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Gradient Boosting is a versatile ensemble learning method used by data scientists and machine learning practitioners to build highly accurate predictive models. Whether you're a beginner or an experienced data scientist, this video has something for everyone.

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In this tutorial, we dive deep into gradient boosting within Python using scikit-learn. We start by explaining the theory behind gradient boosting and how it differs from random forests—instead of having separated decision trees, gradient boosting progressively builds models by using results from weaker prediction models and minimizing losses at each level.

We then jump into live coding with the wine dataset from scikit-learn, a classification problem perfect for demonstrating gradient boosting. You'll learn how to import datasets directly from scikit-learn, split data into training and testing sets, and implement the GradientBoostingClassifier. The video covers essential steps including cross-validation scoring to evaluate model performance.

A key focus of this tutorial is hyperparameter tuning using GridSearchCV. We explore three critical parameters: n_estimators (number of trees), learning_rate (which scales the contribution of each tree), and max_depth (the depth of each tree). Through systematic testing, we improve our model accuracy from 92.2% to 94.3%.

Whether you're new to machine learning or looking to add gradient boosting to your toolkit, this hands-on tutorial provides practical examples you can implement immediately. All code is demonstrated step-by-step in Jupyter Notebook, making it easy to follow along and practice.

TIMESTAMPS
00:00 Introduction to Gradient Boosting
00:18 Theory: Gradient Boosting vs Random Forest
01:13 Setting Up Jupyter Notebook
01:46 Importing the Wine Dataset from Scikit-Learn
02:28 Exploring the Dataset
03:05 Train Test Split
04:02 Importing Gradient Boosting Classifier
05:01 Fitting the Model & Cross-Validation Score
05:54 Setting Up Hyperparameter Tuning
07:26 Max Depth Parameter
08:02 Grid Search CV Implementation
09:02 Results & Comparison

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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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