Tree_Regression is a high-performance system designed for optimizing tree regression models through distributed training. The project, orchestrated in a hierarchical architecture, streamlines data loading, model training, and evaluation processes to achieve robust analysis. Leveraging scikit-learn for building precise models on training data and making accurate predictions on test datasets, this tool plays a vital role in implementing machine learning models effectively.

Tree-based regression is a powerful machine learning technique used for predicting continuous outcomes. Decision trees and random forests are popular algorithms for tree-based regression tasks. This repository provides implementations and examples of tree-based regression techniques in Python.

Decision trees are versatile models that learn simple decision rules from the data. They recursively split the data into subsets based on the values of input features, making predictions by averaging the target values within each leaf node. Decision trees are interpretable and can handle both numerical and categorical data.

Suppose you want to predict the price of a house based on features such as square footage, number of bedrooms, and location. A decision tree could learn rules such as "If square footage <= 1500 and number of bedrooms <= 2, predict price = $200,000" and so on.

Random forest regression is an ensemble learning method that builds multiple decision trees and combines their predictions to produce more robust and accurate results. Each tree in the forest is trained on a random subset of the data and features, reducing overfitting and improving generalization performance.

Continuing with the house price prediction example, a random forest could build multiple decision trees, each trained on a different subset of features and data instances. The final prediction would be the average prediction of all the individual trees, resulting in a more stable and accurate model.

This repository includes sample datasets in CSV format that can be used to practice tree-based regression algorithms.

└── Tree_Regression/
    ├── Data
    │   ├── prediction.csv
    │   ├── prediction_new.csv
    │   ├── test.csv
    │   └── train.csv
    ├── README.md
    ├── Tree_Regression.ipynb
    └── requirements.txt

Requirements

Ensure you have the following dependencies installed on your system:

• JupyterNotebook

1. Clone the Tree_Regression repository:

git clone https://github.com/sumony2j/Tree_Regression.git

2. Change to the project directory:

cd Tree_Regression

3. Install the dependencies:

pip install -r requirements.txt

Use the following command to run Tree_Regression:

jupyter nbconvert --execute notebook.ipynb

Contributions are welcome! Here are several ways you can contribute:

• Submit Pull Requests: Review open PRs, and submit your own PRs.
• Join the Discussions: Share your insights, provide feedback, or ask questions.
• Report Issues: Submit bugs found or log feature requests for Tree_regression.