TreeModelVis: Enhanced Tree-Based Model Visualization

TreeModelVis is a versatile Python toolkit for visualizing and customizing tree-based models, including decision trees and ensembles like Random Forests and Gradient Boosting. Engineered for seamless integration with scikit-learn, TreeModelVis delivers enhanced interpretability and detailed visualization capabilities, making it an indispensable tool for model analysis and presentation.

Features

• Versatile Visualization: Create detailed, customizable graphics to visualize decision trees and tree ensembles.

• Out-of-Sample Data Analysis: Evaluate out-of-sample data and compare its error alignment with the training error, offering insights into the model's generalization.

• Data Distribution Insights: Gain an understanding of data distribution and model decision patterns to improve interpretability.

• Individual Decision Logic Analysis: Utilize draw_path to visualize and understand the decision-making process for specific instances, providing deep insights into how models make predictions.

• Worst Performing Path Identification: Identify and analyze the paths within the model that result in the highest error rates, helping to pinpoint areas for improvement.

• scikit-learn Integration: Seamlessly integrates with existing scikit-learn workflows for a smooth user experience.

• User-Friendly: Designed with simplicity in mind, enabling both beginners and experienced practitioners to leverage its capabilities efficiently.

Quickstart

To get started with TreeModelVis, install the package using pip:

pip install TreeModelVis

Or clone the repository:

git clone https://github.com/yourusername/TreeModelVis.git
cd TreeModelVis
import sys
from sklearn.model_selection import train_test_split
import pandas as pd
import os

sys.path.insert(0, os.path.abspath(os.path.join("TreeModelVis", 'source')))

from TreeModel import TreeModel
file_path = 'TreeModelVis/data/diabetes.csv'
df_diabetes = pd.read_csv(file_path)
X_diabetes = df_diabetes.drop('Outcome', axis=1)
y_diabetes = df_diabetes['Outcome']
X_train_diabetes, X_test_diabetes, y_train_diabetes, y_test_diabetes = train_test_split(
            X_diabetes, y_diabetes, test_size=0.2, random_state=42)
class_names_diabetes = ['No', 'Yes']
tree_model = TreeModel(
            model_type='decision_tree',
            model_params={'max_depth': 3},
            X_train=X_train_diabetes,
            y_train=y_train_diabetes,
            class_names=class_names_diabetes
        )
filename = os.path.join("graphical_output", "test_TreeModel_diabetes")
output_path = tree_model.custom_plot_tree(filename=filename)
print(output_path)

Example Usage

For a hands-on introduction to TreeModelVis, including how to visualize decision trees and analyze model performance, check out our Example Notebook.

This notebook guides you through the steps of using TreeModelVis with practical examples, showcasing how to leverage the toolkit for enhanced model visualization and interpretation. Whether you're a beginner or an experienced practitioner, the notebook provides valuable insights into making the most out of TreeModelVis capabilities.