Ultimate ML Bootcamp #6: Advanced Decision Tree Techniques

Name: Ultimate ML Bootcamp #6: Advanced Decision Tree Techniques
Rating: 4.5 (1 reviews)

Master the Fundamentals of Advanced Decision Tree Techniques

Created byMiuul Data Science & Deep Learning

Last updated 8/2024

English

What you'll learn

Understand the principles and applications of Random Forest.
Master Gradient Boosting Machines and their implementations.
Implement and tune advanced ensemble methods like XGBOOST.
Analyze and interpret feature importance in complex models.

Course content

1 section • 15 lectures • 1h 41m total length

Course Materials0:03
What is Random Forest?11:52
Application: Random Forest13:06
Introduction to GBM I10:26
Introduction to GBM II19:23
Explore additive modeling in gbm by starting with a base predictor and iteratively modeling residuals with delta models, boosting predictions through gradient descent and residual-based corrections.
Application: GBM6:25
What is XGBOOST?2:18
Application: XGBOOST6:46
Explore XGBoost core concepts by building a model, performing cross validation, tuning hyperparameters such as n_estimators, learning rate, max depth, and subsample by tree, then assess accuracy and f1 score.
What is LightGBM?2:14
Application: LightGBM10:10
What is CATBOOST?2:06
Application: CATBOOST3:10
Feature Importance3:48
Explore feature importance across models like random forest, GBM, XGBoost, and LightGBM, highlighting top predictors such as glucose, BMI, and age, and discuss feature engineering to keep models simple.
Random Search5:53
Learning Curves4:15

Requirements

Familiarity with Python is beneficial as the course will involve practical coding exercises.

Description

Welcome to the sixth chapter of Miuul’s Ultimate ML Bootcamp—an advanced series designed to deepen your expertise in machine learning with a focus on ensemble methods. This chapter, Ultimate ML Bootcamp #6: Advanced Decision Tree Techniques, builds on your foundational knowledge and introduces you to sophisticated models used widely in both classification and regression tasks.

In this chapter, we will explore a range of ensemble techniques that enhance predictive performance and robustness. You'll begin by understanding the concept and application of Random Forest, followed by detailed sessions on Gradient Boosting Machines (GBM), including practical applications and optimization strategies. Furthermore, we will delve into newer, cutting-edge methods like XGBOOST, LightGBM, and CATBOOST, examining each for their unique strengths and use-cases.

Practical insights into model evaluation, feature importance, and the use of techniques such as random search and learning curves to optimize model performance will be covered. Hands-on sessions will help you apply these concepts to real-world data, focusing on tuning hyperparameters and assessing model effectiveness.

This chapter is crafted to provide a balance of deep theoretical knowledge and extensive practical experience, empowering you to master these advanced techniques and apply them confidently in your projects. By the end of this chapter, you will have a comprehensive understanding of advanced decision tree techniques, positioning you to take on complex challenges in machine learning.

We are excited to support your continued learning as you navigate through the advanced landscapes of ensemble methods. Let’s embark on this educational journey and unlock further dimensions of your analytical capabilities!

Who this course is for:

For intermediate learners in data science and machine learning who are looking to deepen their understanding of predictive modeling techniques
Ideal for those who have a foundational knowledge of Python and statistics but wish to expand their skills in specific machine learning algorithms.