This project uses Weka to analyze the "Car Evaluation" dataset with decision trees, comparing model performance on 70/30 and 50/50 data splits. It includes accuracy, F1-scores, and decision tree ...

The crux of the problem in this field is that efficient algorithms like k -means struggle to distinguish between metastable states effectively. However, more robust methods like density-based ...

Clustering algorithms are indispensable tools in a data scientist's arsenal for exploratory data analysis, pattern recognition, and data-driven decision-making. Understanding the characteristics, ...

Decision tree induction algorithms are well known techniques for assigning objects to predefined categories in a transparent fashion. Most decision tree induction algorithms rely on a greedy top-down ...

While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does ...

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically ...

Data Mining project to implement Quinlan's C4.5 decision tree algorithm from scratch for medical data mining using the Thyroid allbp dataset ...