deep cox mixturesCode for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021
Stars: ✭ 22 (-45%)
Loan-WebML-powered Loan-Marketer Customer Filtering Engine
Stars: ✭ 13 (-67.5%)
ShifuAn end-to-end machine learning and data mining framework on Hadoop
Stars: ✭ 207 (+417.5%)
EmlearnMachine Learning inference engine for Microcontrollers and Embedded devices
Stars: ✭ 154 (+285%)
cheapmlMachine Learning algorithms coded from scratch
Stars: ✭ 17 (-57.5%)
ChefboostA Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
Stars: ✭ 176 (+340%)
Shapley regressionsStatistical inference on machine learning or general non-parametric models
Stars: ✭ 37 (-7.5%)
loloA random forest
Stars: ✭ 37 (-7.5%)
Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
Stars: ✭ 1,908 (+4670%)
Ml ProjectsML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
Stars: ✭ 127 (+217.5%)
cqrConformalized Quantile Regression
Stars: ✭ 152 (+280%)
xforestA super-fast and scalable Random Forest library based on fast histogram decision tree algorithm and distributed bagging framework. It can be used for binary classification, multi-label classification, and regression tasks. This library provides both Python and command line interface to users.
Stars: ✭ 20 (-50%)
Orange3🍊 📊 💡 Orange: Interactive data analysis
Stars: ✭ 3,152 (+7780%)
rfvisA tool for visualizing the structure and performance of Random Forests 🌳
Stars: ✭ 20 (-50%)
InfiniteboostInfiniteBoost: building infinite ensembles with gradient descent
Stars: ✭ 180 (+350%)
wetlandmapRScripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS
Stars: ✭ 16 (-60%)
Machine Learning Is All You Need🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
Stars: ✭ 173 (+332.5%)
scorubyRuby Scoring API for PMML
Stars: ✭ 69 (+72.5%)
Benchm MlA minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Stars: ✭ 1,835 (+4487.5%)
eForestThis is the official implementation for the paper 'AutoEncoder by Forest'
Stars: ✭ 71 (+77.5%)
survtmleTargeted Learning for Survival Analysis
Stars: ✭ 18 (-55%)
Github-Stars-PredictorIt's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.
Stars: ✭ 34 (-15%)
AIML-ProjectsProjects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
Stars: ✭ 85 (+112.5%)
Decision Tree JsSmall JavaScript implementation of ID3 Decision tree
Stars: ✭ 253 (+532.5%)
dlime experimentsIn this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (-47.5%)
QuickmlA fast and easy to use decision tree learner in java
Stars: ✭ 230 (+475%)
handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+612.5%)
receiptdIDReceipt.ID is a multi-label, multi-class, hierarchical classification system implemented in a two layer feed forward network.
Stars: ✭ 22 (-45%)
survHESurvival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
Stars: ✭ 32 (-20%)
RandomforestexplainerA set of tools to understand what is happening inside a Random Forest
Stars: ✭ 175 (+337.5%)
Machine-Learning-ModelsIn This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Stars: ✭ 30 (-25%)
Machine Learning ModelsDecision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
Stars: ✭ 160 (+300%)
Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✭ 2,197 (+5392.5%)
Machine Learning In RWorkshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Stars: ✭ 144 (+260%)
hdnomBenchmarking and Visualization Toolkit for Penalized Cox Models
Stars: ✭ 36 (-10%)
pydata-london-2018Slides and notebooks for my tutorial at PyData London 2018
Stars: ✭ 22 (-45%)
pykitmlMachine Learning library written in Python and NumPy.
Stars: ✭ 26 (-35%)
Isl PythonSolutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Stars: ✭ 108 (+170%)
Patch-GCNContext-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
Stars: ✭ 63 (+57.5%)
bessBest Subset Selection algorithm for Regression, Classification, Count, Survival analysis
Stars: ✭ 14 (-65%)
Predicting real estate prices using scikit LearnPredicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
Stars: ✭ 78 (+95%)
TFDeepSurvCOX Proportional risk model and survival analysis implemented by tensorflow.
Stars: ✭ 75 (+87.5%)
goscoreGo Scoring API for PMML
Stars: ✭ 85 (+112.5%)
Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-LearnFormed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…
Stars: ✭ 41 (+2.5%)