Predictive Power: A Practical Guide to Decision Trees & Random Forests

Introduction

Decision Trees and Random Forests are fundamental algorithms in machine learning, prized for their interpretability, flexibility, and predictive accuracy. They are used across a wide range of industries to solve complex classification and regression problems, from predicting customer churn to diagnosing medical conditions. This training course provides a deep dive into these powerful algorithms, focusing on the practical skills and theoretical understanding needed to implement them effectively.

This five-day program is designed for aspiring and current data scientists, and data analysts who want to go beyond simple model application and truly understand the mechanics of tree-based methods. Through hands-on exercises and real-world case studies, you will learn to build, optimize, and interpret models that provide clear, actionable insights. By the end of this course, you will be equipped to select the right algorithm for the job and confidently explain your model's decisions to stakeholders.

Duration 5 days

Target Audience This course is intended for data analysts, data scientists, machine learning engineers, and statisticians who have a foundational knowledge of Python and basic machine learning concepts.

Objectives

  1. To understand the core principles and mechanics of a Decision Tree algorithm.
  2. To learn how to build and visualize a Decision Tree for classification and regression.
  3. To understand and address common issues like overfitting and instability.
  4. To grasp the concept of an ensemble method and its application in Random Forests.
  5. To build and tune a Random Forest model for improved predictive performance.
  6. To learn how to interpret and explain the results of a tree-based model.
  7. To compare and contrast Decision Trees and Random Forests with other algorithms.
  8. To handle different types of data, including categorical and numerical features.
  9. To use advanced techniques like feature importance and partial dependence plots.
  10. To apply learned concepts to real-world, industry-specific datasets.

Course Modules

Module 1: Introduction to Supervised Learning

  • A brief recap of supervised learning principles.
  • The difference between classification and regression.
  • The role of features and target variables.
  • A conceptual overview of Decision Trees.
  • The importance of model performance metrics.

Module 2: The Inner Workings of Decision Trees

  • How a Decision Tree makes decisions: splits and leaves.
  • Key concepts: entropy, Gini impurity, and information gain.
  • The algorithm for building a tree from data.
  • The dangers of overfitting and how to identify it.
  • Visualization of a Decision Tree.

Module 3: Building and Optimizing Decision Trees

  • Pre-processing data for tree-based models.
  • Building a Decision Tree using a library like Scikit-learn.
  • Hyperparameter tuning for controlling tree complexity.
  • Techniques for handling missing values and imbalanced data.
  • Cross-validation as a method for robust model evaluation.

Module 4: The Power of Ensemble Methods

  • The "wisdom of the crowd" concept.
  • Introduction to ensemble learning: bagging and boosting.
  • The core idea behind a Random Forest model.
  • How a Random Forest reduces variance and improves stability.
  • Visualizing the ensemble process.

Module 5: Implementing and Tuning Random Forests

  • Building a Random Forest for a classification task.
  • Key hyperparameters for Random Forests: n_estimators, max_depth, min_samples_leaf.
  • The oob_score for out-of-bag evaluation.
  • Best practices for hyperparameter search.
  • Using a Random Forest for regression problems.

Module 6: Interpreting Tree-Based Models

  • Understanding feature importance in Decision Trees and Random Forests.
  • The difference between Gini importance and permutation importance.
  • Creating partial dependence plots to visualize feature effects.
  • Explaining model predictions with tools like SHAP.
  • Communicating model insights to non-technical audiences.

Module 7: Handling Different Data Types

  • The unique challenges of categorical variables.
  • One-hot encoding and its impact on tree-based models.
  • Strategies for high-cardinality categorical features.
  • Working with mixed numerical and categorical data.
  • Practical exercises with real-world datasets.

Module 8: Comparing Models

  • When to use Decision Trees vs. Random Forests.
  • The trade-off between interpretability and predictive power.
  • A comparison with other popular algorithms like Logistic Regression and SVM.
  • Understanding when tree-based models may not be the best choice.
  • The role of model stacking and blending.

Module 9: Advanced Random Forest Topics

  • The mechanics of feature subsampling and data bootstrapping.
  • Understanding the Random Forest algorithm in more detail.
  • Out-of-bag error estimation and its advantages.
  • The importance of the "randomness" in Random Forests.
  • Using Random Forests for anomaly detection.

Module 10: Case Studies in Practice

  • Using Decision Trees to segment customers for marketing.
  • Building a Random Forest to predict loan default.
  • Classifying medical images for diagnosis.
  • Predicting house prices with a Random Forest Regressor.
  • A hands-on project to apply all learned concepts.

Module 11: Gradient Boosting Machines

  • An introduction to boosting algorithms.
  • The core idea behind Gradient Boosting.
  • Key differences between Gradient Boosting and Random Forests.
  • Building a Gradient Boosting model with popular libraries.
  • Practical examples and applications.

Module 12: Ethical Considerations

  • The risk of bias in training data for tree-based models.
  • The importance of fairness and explainability.
  • Strategies for identifying and mitigating algorithmic bias.
  • Case studies of unethical applications of Decision Trees.
  • The importance of responsible AI development.

Module 13: The Future of Tree-Based Models

  • Emerging trends and research in ensemble learning.
  • The development of new algorithms and libraries.
  • The role of Decision Trees in the context of deep learning.
  • A final review of course objectives.
  • Building a personal action plan for continued learning.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Predictive Power: A Practical Guide To Decision Trees & Random Forests in Namibia
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