What This Document Is
This document represents the ninth assignment for Pace University’s Data Mining (CS 619) course. It focuses on applying Support Vector Machine (SVM) algorithms using the WEKA data mining tool to the well-known Iris dataset. The assignment requires students to analyze model outputs, including classifier parameters, error rates, and visualizations of misclassified instances, under both percentage split and cross-validation methodologies.
Why This Document Matters
This assignment is intended for students enrolled in the CS 619 Data Mining course. It serves as a practical exercise to reinforce understanding of SVM implementation, model evaluation metrics (MAE, RMSE), and the interpretation of results within a data mining environment. Successful completion demonstrates proficiency in using WEKA for classification tasks and analyzing model performance.
Common Limitations or Challenges
This document is a completed assignment; it does not provide instruction on data mining concepts or WEKA usage. It assumes prior knowledge of SVMs and the WEKA interface. It showcases *an* example solution, but does not offer guidance on troubleshooting or alternative approaches.
What This Document Provides
The full document includes: detailed outputs from the WEKA Explorer environment when using the SMO classifier on the Iris dataset, including learned classifier equations (hyperplanes) for different class pairings; quantitative results such as incorrectly classified instance counts, MAE, and RMSE for both percentage split and cross-validation; and a description of visualized errors. This preview only provides a glimpse of the results obtained, specifically the classifier models and error metrics. It does *not* include the visualizations or a complete explanation of the results.
