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Ultimate ML Bootcamp #5: Classification & Regression Trees
Rating: 3.5 out of 5(3 ratings)
660 students

Ultimate ML Bootcamp #5: Classification & Regression Trees

Master the Fundamentals of CART
Created byMiuul Data Science & Deep Learning
Last updated 8/2024
English

What you'll learn

  • Grasp the fundamental concepts and mechanics behind Classification and Regression Trees.
  • Become proficient in evaluating model performance using metrics like Gini impurity and entropy.
  • Learn to preprocess data effectively for optimal CART model performance.
  • Apply CART to real-world scenarios, interpreting and improving model outcomes through practical examples.

Course content

1 section • 16 lectures • 1h 57m total length

  • Course Materials0:03
  • Introduction to CART I6:32

    Explore CART, the classification and regression tree method, to build simple rules from data and split into homogeneous groups, laying the foundation for random forests.

  • Introduction to CART II8:17

    Explore the CART framework and how decision trees split data into rules, using thresholds and metrics to minimize error, with leaves predicting averages.

  • Introduction to CART III9:43
  • Introduction to CART IV10:13
  • Installations3:39

    Set up cart, explore from basics to advanced tree methods, and cover hyperparameter optimization, learning curves, visualization, rule extraction, and generating Python, SQL, and Excel outputs.

  • Modelling12:55
  • Hyperparameter Optimization11:24
  • Final CART Model3:03

    Deploy the final cart model using the decision tree classifier, tune min sample split and max depth via setparams and getparams, and evaluate with cross-validation metrics: accuracy, F1, and AUC.

  • Feature Importance4:18

    Rank variables by their contribution to regression or classification tasks, minimize SSA and reduce Gini and entropy, then visualize their importance and note glucose, BMI, and age as top predictors.

  • Learning Curves13:43
  • Visualization8:05

    visualize a classification and regression tree, generate a png diagram of the model, and explore splitting criteria such as gini and entropy with adjustable maximum depth.

  • Decision Rules2:10
  • Extracting Python Codes for Decision Rules7:33

    Extract python codes, sql queries, and excel formulas to derive decision rules from a decision tree and deploy the model inside the database for in-database predictions.

  • Prediction2:28
  • Saving Models12:58

Requirements

  • Familiarity with Python is beneficial as the course will involve practical coding exercises.
  • A basic understanding of statistics will help in grasping model evaluation techniques.

Description

Welcome to the fifth chapter of Miuul’s Ultimate ML Bootcamp—a comprehensive series designed to elevate your expertise in machine learning and artificial intelligence. This chapter, Ultimate ML Bootcamp #5: Classification and Regression Trees (CART), builds upon the skills you've developed and introduces you to an essential machine learning technique used widely in classification and regression tasks.

In this chapter, we will thoroughly explore the CART methodology. You'll start by learning the theoretical foundations of how decision trees are constructed, including the mechanisms behind splitting criteria and the strategies for optimizing tree depth.

Moreover, we will delve into various model evaluation metrics specific to CART and explore techniques to prevent overfitting. Practical application of CART in solving real-world problems will be emphasized, with a focus on tuning hyperparameters and assessing feature importance.

This chapter aims to provide a balance of deep theoretical insights and hands-on practical experience, enabling you to implement and optimize CART models effectively. By the end of this exploration, you will be well-equipped with the knowledge to use CART in your own projects and further your journey in machine learning.

We are excited to support your continued learning as you delve into the dynamic world of Classification and Regression Trees. Let’s begin this enlightening chapter and unlock new 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.

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