Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better results than gradient boosted decision trees (GBDT) on a number of datasets and it has been used to win a few Kaggle competitions. Unlike the traditional boosted decision tree approach, RGF works directly with the underlying forest structure. RGF integrates two ideas: one is to include tree-structured regularization into the learning formulation; and the other is to employ […]

Python package for Bayesian Machine Learning with scikit-learn API Installing & Upgrading package pip install https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip pip install –upgrade https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip Algorithms ARD Models Relevance Vector Regression (version 2.0) code, tutorial Relevance Vector Classifier (version 2.0) code, tutorial Type II Maximum Likelihood ARD Linear Regression code Type II Maximum Likelihood ARD Logistic Regression code, tutorial Variational Relevance Vector Regression

Simple machine learning library / 簡單易用的機器學習套件 Installation Tutorial Algorithm Perceptron Perceptron Binary Classification Learning Algorithm Perceptron Multi Classification Learning Algorithm Pocket Perceptron Binary Classification Learning Algorithm Pocket Perceptron Multi Classification Learning Algorithm Regression Linear Regression Learning Algorithm Linear Regression Binary Classification Learning Algorithm Linear Regression Multi Classification Learning Algorithm Ridge Regression Learning Algorithm Ridge Regression Binary Classification Learning Algorithm Ridge Regression Multi Classification Learning Algorithm Kernel Ridge Regression Learning Algorithm Kernel Ridge Regression Binary Classification    

py_neuromodulation The py_neuromodulation toolbox allows for real time capable processing of multimodal electrophysiological data. The primary use is movement prediction for adaptive deep brain stimulation. Find the documentation here https://neuromodulation.github.io/py_neuromodulation/ for example usage and parametrization. Setup For running this toolbox first create a new virtual conda environment: conda env create –file=env.yml –user The main modules include running real time enabled feature preprocessing based on iEEG BIDS data. Different features can be enabled/disabled and parametrized in the `https://github.com/neuromodulation/py_neuromodulation/blob/main/pyneuromodulation/nm_settings.json>`_. The current implementation […]