Classification Example - Weka Typing CST Test
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Classification Example — Weka Code
An example showing how to perform a simple classification task using Weka's GUI or Java API.
// Using Weka GUI:
// 1. Open Weka Explorer
// 2. Load dataset (e.g., iris.arff)
// 3. Select 'Classify' tab
// 4. Choose classifier (e.g., J48 decision tree)
// 5. Click 'Start' to train and evaluate
// Using Java API:
// Instances data = new Instances(new BufferedReader(new FileReader("iris.arff")));
// data.setClassIndex(data.numAttributes() - 1);
// Classifier cls = new J48();
// cls.buildClassifier(data);Weka Language Guide
Weka (Waikato Environment for Knowledge Analysis) is an open-source suite of machine learning software written in Java, providing a collection of visualization tools and algorithms for data analysis and predictive modeling with a GUI, command-line interface, and Java API.
Primary Use Cases
- ▸Classification of tabular data
- ▸Regression and predictive modeling
- ▸Clustering and unsupervised learning
- ▸Feature selection and data preprocessing
- ▸Visualization of data and model outputs
Notable Features
- ▸GUI for designing experiments and workflows
- ▸Large collection of built-in machine learning algorithms
- ▸Data preprocessing and attribute selection tools
- ▸Visualization of datasets and classifier performance
- ▸Command-line interface and Java API support
Origin & Creator
Weka was developed at the University of Waikato, New Zealand, starting in 1993, to provide an easy-to-use tool for teaching, research, and practical machine learning experiments.
Industrial Note
Weka is widely used in academic research, teaching, rapid prototyping of ML models, and small to medium-scale data analysis projects in industries where Java integration is useful.