Learn WEKA with Real Code Examples

Best for datasets that fit in memory

Use filters to reduce dimensionality for large datasets

Cross-validation helps prevent overfitting

GUI is slower than scripting for batch experiments

Lightweight, but Java heap size may need adjustment for large data

Secure saved model files

Handle sensitive data carefully in ARFF/CSV files

Audit usage of Weka in multi-user systems

Validate input datasets before processing

Ensure reproducibility via consistent Java versions

Track evaluation metrics (accuracy, ROC, F-measure)

Compare classifiers using cross-validation

Visualize attribute importance and model outputs

Log results for research or reporting

Monitor runtime for memory-intensive tasks

Document scripts and workflows

Organize datasets and models

Maintain consistent preprocessing steps

Version control CLI scripts and Java code

Ensure reproducibility with fixed random seeds