Build & Evaluate Decision Trees for ML

Overview

Are you ready to master one of machine learning’s most powerful and interpretable algorithms? This course will guide you through the complete journey of understanding, building, and evaluating decision tree models using Java, the enterprise-standard programming language. You’ll start by exploring the core concepts, how decision trees partition data, why splitting criteria such as entropy and the Gini index matter, and when decision trees outperform other algorithms. From there, you’ll move into hands-on implementation, using industry-standard tools like Weka’s intuitive GUI and Java API along with Smile’s high-performance library to develop, tune, and deploy models. Through practical exercises, you’ll learn to configure hyperparameters, balance rapid prototyping with production-ready design, and apply robust model evaluation techniques such as confusion matrices, cross-validation, and key performance metrics. Aspiring and experienced data scientists, Java developers, and machine learning engineers seeking to build, evaluate, and interpret decision tree models for real-world applications in finance, healthcare, and business analytics. Basic Java programming experience, understanding of object-oriented concepts, and fundamental knowledge of data science principles required. By the end of the course, you’ll be equipped to detect and reduce overfitting, optimize model performance, and effectively communicate insights to technical and business stakeholders alike.

Syllabus

Decision Tree Fundamentals

Explore decision tree foundations including tree structure, classification mechanics, splitting criteria like entropy and Gini index, and how recursive partitioning creates predictive models for machine learning applications.

Building Decision Trees in Java

Build decision tree classifiers using Weka's GUI and Java API, then explore Smile library for modern implementations. Configure hyperparameters, train models on real datasets, and export trained models.

Evaluating Decision Tree Performance

Evaluate decision tree performance using confusion matrices, accuracy metrics, precision, recall, and F1-scores. Apply cross-validation techniques to assess model generalization. Learn to interpret results and identify overfitting.