This repository is intended for the exploration and implementation of Decision Tree models using the Scikit-learn library. Decision trees are a popular machine learning technique for classification and regression tasks, offering interpretable and visual representations of decision-making processes.
The primary objectives of this repository are to:
Document the implementation of decision tree algorithms using Scikit-learn on various datasets.
Analyze the impact of different hyperparameters, such as tree depth and splitting criteria, on model performance.
Provide visual representations of decision trees to enhance understanding of their structure and decision-making processes.
Data Preparation: Preprocess datasets, including cleaning, normalization, and splitting into training and testing sets.
Implement decision tree models using Scikit-learn, focusing on both classification and regression tasks.
Assess model performance using metrics like accuracy, precision, recall, and F1-score.
Create visual representations of decision trees to improve interpretability.
Primary programming language for implementing decision trees.
Library used for building and evaluating decision tree models.
For data manipulation and preprocessing.
For data visualization.
As I continue to explore decision tree models, I plan to investigate advanced variations such as Random Forests and Gradient Boosted Trees and apply these techniques to various datasets.