In this tutorial, we covered the essential steps for implementing a Decision Tree classifier in Java using the Weka library. You learned about data preparation, model building, and evaluating the performance of your model.

Feb 3, 2021 · This year, we are going to implement, through a succession of assignments, a simplified version of a useful machine learning technique, called decision tree classification.

Mar 17, 2025 · Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.

This repository features a Java implementation of a Decision Tree Classifier, demonstrating the algorithm's core concepts, including tree building, predictions, and model evaluation.

Java decision tree implementation for the the "Titanic - Machine Learning from Disaster" dataset [1]. The classifier achieves an accuracy of 0.7249 on the Kaggle test data set. The implementation is based on the C4.5 algorithm developed by Ross Quinlan [2].

Aug 26, 2022 · It easy to correlate and understand hidden math behind it. It can be used both for classification and regression problem. Mathematically its driven by two concepts Gini Impurity and Information...

May 21, 2023 · This repository features a Java implementation of a Decision Tree Classifier, demonstrating the algorithm's core concepts, including tree building, predictions, and model evaluation.

Implementing a decision tree in Java is a straightforward yet powerful method for data analysis. By following the steps outlined in this tutorial, you can create your own decision tree models and apply them to various datasets.

In this tutorial, you've learned how to implement a decision tree classifier in Java using the Weka library. We explored the concept of decision trees, walked through the process of creating and evaluating a model, and understood how to make predictions based on new data.

Instantly share code, notes, and snippets. /* Machine learning supervised algorithm to decide (classify) data based on previous data set. * This algorithm is commonly called as decision tree. //Meta data about the text file. { /*Create 2D array to store data. Usually last element in row represents class. * class with the related attribute.