mlr-cpp 1.1.0

A high-performance Multiple Linear Regression model implemented in C++ with a clean Python API via Pybind11 bindings

mlr_cpp – Fast C++ Multiple Linear Regression with Python API

mlr_cpp is a high-performance Multiple Linear Regression library implemented in C++ and exposed to Python using pybind11. It is designed for speed, delivering results over 10x faster than scikit-learn, while providing a user-friendly pandas-compatible API.

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Key Features

• Blazing fast C++ core, with Pybind11 for seamless Python integration
• Supports full model diagnostics: R², Adjusted R², MAE, MSE, F-test, T-statistics, P-values
• Automatically checks for multicollinearity and sufficient data
• Equation generation with smart rounding and absolute tolerance
• Compatible with pandas DataFrames

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Benchmarks with sklearn

coefficients

mlr_cpp: [3.98827526e+00 3.98919944e+00 8.77169801e+00 3.74320006e+00
          3.38302087e-04 1.10138110e-02]
sklearn: [3.98919944e+00 8.77169801e+00 3.74320006e+00 3.38302087e-04
          1.10138110e-02]

Runtimes

mlr_cpp: 0.000344s
sklearn: 0.004404s

$R^2$ Scores

mlr_cpp: 0.987661
sklearn: 0.987661

mlr_cpp provides identical accuracy while being ~10x faster than scikit-learn

Installation

pip install mlr_cpp

Optional Extras

pip install mlr_cpp[examples]  # for examples, plotting
pip install mlr_cpp[dev]       # for development tools

Example Usage

import pandas as pd
from mlr_wrapper import MLRWrapper

df = pd.read_csv("your_data.csv")
model = MLRWrapper(df, target_col="mpg")

model.fit()
eqn, predictors, tests = model.get_model_summary(tstats=True)

print(eqn)
print(predictors)
print(tests)

Prediction

model.predict(new_df)

Model Summary

You can retrieve a complete summary of the model including the regression equation, coefficients, p-values, and test metrics:

eqn, predictor_summary, model_tests = model.get_model_summary(tstats=True)

print(eqn)                  # Prints the regression equation
print(predictor_summary)    # DataFrame with coefficients, p-values, and t-statistics
print(model_tests)          # DataFrame with metrics like R², MAE, MSE, etc.

Evaluation Metrics

After fitting the model, mlr_cpp provides access to standard evaluation metrics for performance diagnostics:

model.get_R2()           # Coefficient of Determination (R²)
model.get_AdjustedR2()   # Adjusted R²
model.get_MAE()          # Mean Absolute Error
model.get_MSE()          # Mean Squared Error
model.get_ftest()        # F-statistic of the regression model
model.get_TStatistics()  # T-statistics for each predictor
model.get_PValues()      # P-values corresponding to each predictor
model.get_model_tests()  # Summary DataFrame containing major evaluation metrics

👨‍💻 Author

Sidhant Raj Khati

🌐︎ Website: sidhantkhati.com
💼 LinkedIn: LinkedIn
🔗 GitHub: Sidhant-1299/mlr_cpp

📄 License

This project is licensed under the MIT License