optimal-regressors 0.2.0

A Python library to find optimal configurations for regressors

OptimalRegressors

OptimalRegressors is a Python library designed to find the optimal configurations for decision tree and random forest regressors. It automates hyperparameter tuning, such as determining the optimal number of leaf nodes, using validation data to improve model performance.

Features

• Automatically optimizes hyperparameters for:
  • DecisionTreeRegressor
  • RandomForestRegressor
• Allows custom candidate values for hyperparameter tuning.
• Optionally fits the optimized model for direct use.
• Simple, lightweight, and easy to use.

Installation

To install OptimalRegressors, follow the steps below:

Clone the Repository

git clone https://github.com/RehanTaneja/OptimalRegressors.git
cd OptimalRegressors

Install Dependencies

Use pip to install the required dependencies:

pip install -r requirements.txt

Usage

Optimal Decision Tree Regressor

• Parameters:
  • trainX,trainy Training data (features & labels)
  • valX,valy Validation data (features & labels)
  • candidate_nodes (list, optional): A list of max_leaf_nodes values to test. Default: [5, 50, 500, 5000]
  • fit (bool, optional): Whether to fit the returned model on the training data. Default: False
• Returns:
  • model: A DecisionTreeRegressor instance with the optimal configuration
  • nodes: The optimal max_leaf_nodes value

Find the best max_leaf_nodes for a decision tree:

from OptimalRegressor import OptimalDecisionTreeRegressor

# Example data (replace with your dataset)
trainX, trainy = [[1], [2], [3]], [2.5, 3.5, 5.0]
valX, valy = [[1.5], [2.5]], [3.0, 4.0]

# Get the optimal DecisionTreeRegressor
model, nodes = OptimalDecisionTreeRegressor(trainX, trainy, valX, valy)

print("Optimal max_leaf_nodes:", nodes)

Optimal Random Forest Regressor

• Parameters:
  • trainX, trainy: Training data (features and labels)
  • trainX, trainy: Training data (features and labels)
  • candidate_nodes (list, optional): A list of max_leaf_nodes values to test. Default: [5, 50, 500, 5000]
  • fit (bool, optional): Whether to fit the returned model on the training data. Default: False
• Returns:
  • model: A RandomForestRegressor instance with the optimal configuration
  • nodes: The optimal max_leaf_nodes value

Find the best max_leaf_nodes value for a random forest regressor

from OptimalRegressor import OptimalRandomForestRegressor

# Example data (replace with your dataset)
trainX, trainy = [[1], [2], [3]], [2.5, 3.5, 5.0]
valX, valy = [[1.5], [2.5]], [3.0, 4.0]

# Get the optimal RandomForestRegressor
model, nodes = OptimalRandomForestRegressor(trainX, trainy, valX, valy)

print("Optimal max_leaf_nodes:", nodes)

Repository Structure

OptimalRegressors/ │ ├── OptimalRegressor.py # Main library file ├── requirements.txt # Python dependencies ├── LICENSE # License information └── README.md # Project documentation