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
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RgfHome repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.
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supervised-machine-learningThis repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…
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Neural Backed Decision TreesMaking decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
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ProtoTreeProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
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cheapmlMachine Learning algorithms coded from scratch
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stackgbm🌳 Stacked Gradient Boosting Machines
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lleavesCompiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
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urb-studies-predicting-gentrificationThis repo is intended to support replication and exploration of the analysis undertaken for our Urban Studies article "Understanding urban gentrification through Machine Learning: Predicting neighbourhood change in London".
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XaiXAI - An eXplainability toolbox for machine learning
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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!
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InfiniteboostInfiniteBoost: building infinite ensembles with gradient descent
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Orange3🍊 📊 💡 Orange: Interactive data analysis
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goscoreGo Scoring API for PMML
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rfvisA tool for visualizing the structure and performance of Random Forests 🌳
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interpretable-mlTechniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
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Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
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ShapA game theoretic approach to explain the output of any machine learning model.
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InterpretFit interpretable models. Explain blackbox machine learning.
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LightgbmA fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
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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
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MLDay18Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
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ImodelsInterpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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linear-treeA python library to build Model Trees with Linear Models at the leaves.
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TpotA Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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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)
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SporfThis is the implementation of Sparse Projection Oblique Randomer Forest
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handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
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arboretoA scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
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thesaurusTT Hackathon 2018 - Autocomplete for Visual Programming Nodes
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cartifyShopping Cart implementation to serve as a shell for building e-commerce apps | Angular 7, Node.js, MongoDB
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oomstoreLightweight and Fast Feature Store Powered by Go (and Rust).
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Machine learning trading algorithmMaster's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals
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deepchecksTest Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
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