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LightGBM Made Simple

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Overview

You'll explore LightGBM's unique architecture, focusing on its efficient leaf-wise tree growth and histogram-based algorithms. You'll learn how to leverage its key parameters for model control, compare its performance to other boosting libraries, and gain hands-on experience.

Syllabus

Unit 1: LightGBM Architecture Essentials

Racing LightGBM Against Traditional Methods
Building Histogram Binning Logic
Complete the Discretization Analysis

Unit 2: Native Categorical Handling

Optimizing Data Types for Machine Learning
Finding What's Missing
Training with Categorical Features
Discovering Your Model's Hidden Drivers

Unit 3: LightGBM Parameter Mastery

Speed versus Accuracy Experiment
Finding the Sweet Spot for Trees
Setting Data Requirements for Leaves
Finding the Perfect Learning Speed
Feature Sampling for Better Models

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