{"id":22452019,"url":"https://github.com/woctezuma/regression","last_synced_at":"2025-07-17T03:31:46.506Z","repository":{"id":37601979,"uuid":"177327039","full_name":"woctezuma/regression","owner":"woctezuma","description":"Gaussian Process Regression vs. Relevance Vector 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Regression\n\n[![Code Quality][codacy-image]][codacy]\n\nThis repository contains Python code to perform 1-D regression with:\n-   [Gaussian Process Regression](https://en.wikipedia.org/wiki/Kriging),\n-   [Relevance Vector Machine](https://en.wikipedia.org/wiki/Relevance_vector_machine).\n\n## Requirements\n\n-   Install the latest version of [Python 3.X](https://www.python.org/downloads/).\n-   Install the required packages:\n\n```bash\npip install -r requirements.txt\npip install https://github.com/JamesRitchie/scikit-rvm/archive/master.zip\n```\n\n## Usage\n\n```bash\npython main.py\n```\n\n## Results\n\nThe ground truth is the sinc function.\n\n### Influence of the number of training samples\n\nThe variable `noise_level` is set to `0.1`.\nThe variable `training_data_range` is set to a large value (`15`).\n\nThe results are shown with increasing number of training samples.\n\n![10 samples](https://github.com/woctezuma/regression/wiki/img/NM0Gerr.png)\n![50 samples](https://github.com/woctezuma/regression/wiki/img/9JbUXcK.png)\n![100 samples](https://github.com/woctezuma/regression/wiki/img/pEmUJyn.png)\n![200 samples](https://github.com/woctezuma/regression/wiki/img/r5yjsGD.png)\n![500 samples](https://github.com/woctezuma/regression/wiki/img/J4krNnB.png)\n\n### Influence of the noise level\n\nThe variable `num_samples` is set to `100`.\nThe variable `training_data_range` is set to a large value (`15`).\n\nThe results are shown with increasing noise level.\n\n![noise 0.0](https://github.com/woctezuma/regression/wiki/img/lq63j83.png)\n![noise 0.1](https://github.com/woctezuma/regression/wiki/img/aw7O2KS.png)\n![noise 0.5](https://github.com/woctezuma/regression/wiki/img/d9dknjW.png)\n![noise 1.0](https://github.com/woctezuma/regression/wiki/img/dFyDuDE.png)\n\n### Influence of range of training data\n\nThe variable `num_samples` is set to `100`.\nThe variable `noise_level` is set to `0.1`.\n\nThe results are shown with increasing range of training data\n\n![range 1](https://github.com/woctezuma/regression/wiki/img/7OyCgMI.png)\n![range 2](https://github.com/woctezuma/regression/wiki/img/az6BP26.png)\n![range 5](https://github.com/woctezuma/regression/wiki/img/fLn9jmF.png)\n![range 10](https://github.com/woctezuma/regression/wiki/img/7tCFWFZ.png)\n![range 15](https://github.com/woctezuma/regression/wiki/img/ugN0nQi.png)\n\n## References\n\n-   Python module [scikit-learn](https://github.com/scikit-learn/scikit-learn)\n-   Documentation: [Gaussian Process](https://scikit-learn.org/stable/modules/gaussian_process.html) with scikit-learn\n-   Python module [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm)\n-   Python module [sklearn-rvm](https://github.com/Mind-the-Pineapple/sklearn-rvm)\n-   Slides about [Relevance Vector Regression](http://lasa.epfl.ch/teaching/lectures/ML_MSc_Advanced/Slides/Lec_IX_NonlinearRegression_Part_I.pdf)\n-   Slides about [Gaussian Process Regression](http://lasa.epfl.ch/teaching/lectures/ML_MSc_Advanced/Slides/Lec_IX_NonlinearRegression_Part_II.pdf)\n\n\u003c!-- Definitions --\u003e\n\n[pyup]: \u003chttps://pyup.io/repos/github/woctezuma/regression/\u003e\n[dependency-image]: \u003chttps://pyup.io/repos/github/woctezuma/regression/shield.svg\u003e\n[python3-image]: \u003chttps://pyup.io/repos/github/woctezuma/regression/python-3-shield.svg\u003e\n\n[codacy]: \u003chttps://www.codacy.com/app/woctezuma/regression\u003e\n[codacy-image]: \u003chttps://api.codacy.com/project/badge/Grade/9b1dad26ffe64ca98fec83ec1ab69c0e\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoctezuma%2Fregression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwoctezuma%2Fregression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoctezuma%2Fregression/lists"}