🧩 Decision Tree and Random Forest Simulator This is an interactive Decision Tree and Random Forest simulation tool where you can visualize model behavior in real-time using any dataset. This tool allows you to fine-tune hyperparameters, select features, and observe how these machine learning algorithms create decision boundaries.
📊 Key Features Upload Your Dataset: Supports CSV datasets with ease. Real-time Visualization: Get real-time visualizations of Decision Tree and Random Forest structures. Hyperparameter Tuning: Adjust parameters like split criteria, max depth, and tree splitters. Interactive and User-Friendly: Powered by Streamlit for a smooth user experience.
Demo [https://youtu.be/x9HUv53ctfc] try here : https://dtrfsimuator.streamlit.app/
📥 Sample Dataset A sample wine quality dataset has been provided for testing. The dataset is sourced from Kaggle. Alternatively, you can use any CSV dataset of your choice.
📈 Hyperparameters Explained Split Criteria:
poisson: For count-based regression tasks. absolute_error: Optimizes based on mean absolute error. squared_error: Default for regression (mean squared error). friedman_mse: An improvement of MSE for better splits. Splitter:
best: Chooses the best possible split. random: Chooses a random split. Max Depth: Limits the depth of the decision tree.
How It Works:- Select Dataset: Upload your CSV file. Choose Features: Select the feature columns and target variable. Adjust Parameters: Tune the hyperparameters to see how models adapt. Visualize Models: View the Decision Tree and Random Forest models in real-time.
🛠️ Tech Stack Frontend: Streamlit Backend: Python Libraries: Scikit-learn, Pandas, Matplotlib
- Install dependencies:
pip install -r requirements.txt - Run the app using Streamlit:
streamlit run app.py
Use Cases Understanding Decision Trees and Random Forest models. Learning how hyperparameters affect machine learning models. Testing performance of different datasets in a visualized environment.