Comprehensive exploration of decision tree classifiers, including data cleaning, model building, and performance evaluation on various datasets.
Decision tree classifiers are versatile and interpretable machine learning algorithms. This repository showcases various aspects of decision tree classifiers, from data preparation to model evaluation.
Data cleaning is a critical step in the machine learning pipeline. In this section, I demonstrate techniques to preprocess and clean datasets to ensure high-quality inputs for the models.
This section covers the implementation of decision tree classifiers, highlighting different approaches and techniques used to build and refine the models.
Evaluating the performance of a model is crucial. Here, I use various metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to assess the effectiveness of the decision tree models.
I plan to expand this repository with more advanced techniques and applications related to decision tree classifiers, including pruning techniques, feature importance analysis, and ensemble methods.
Thank you for exploring my decision tree classifier project. I hope you find it insightful and valuable!