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DTreeViz is a visualization tool that creates detailed, intuitive diagrams of decision trees, helping users understand model decisions, splits, and feature importance clearly.

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📊 DTreeViz: Visualizing Decision Trees on the Iris Dataset

This project demonstrates the use of the DTreeViz library to visualize decision trees trained on the classic Iris flower dataset. The goal is to enhance the interpretability of machine learning models by generating intuitive and detailed visual representations of decision tree structures.

🧠 Key Features:

Trains a DecisionTreeClassifier on the Iris dataset using Scikit-learn.

Uses DTreeViz to generate rich visualizations of the decision tree, including:

Decision boundaries

Feature splits

Class probabilities at nodes

Offers insights into how decision trees make classification decisions.

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DTreeViz is a visualization tool that creates detailed, intuitive diagrams of decision trees, helping users understand model decisions, splits, and feature importance clearly.

Topics

machine-learning-algorithms eda decision-trees iris-dataset dtreeviz

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