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Top 98 random-forest open source projects

Decision Tree Js
Small JavaScript implementation of ID3 Decision tree
Quickml
A fast and easy to use decision tree learner in java
Shifu
An end-to-end machine learning and data mining framework on Hadoop
Infiniteboost
InfiniteBoost: building infinite ensembles with gradient descent
Tensorflow Ml Nlp
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Chefboost
A 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
Randomforestexplainer
A set of tools to understand what is happening inside a Random Forest
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!
Machine Learning Models
Decision 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
Benchm Ml
A 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.).
Machine Learning In R
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Randomforest
随机森林,Random Forest(RF)
Ml Projects
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
Network Intrusion Detection
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
Isl Python
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Predicting real estate prices using scikit Learn
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
Gcforest
This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
Sporf
This is the implementation of Sparse Projection Oblique Randomer Forest
Reproduce Stock Market Direction Random Forests
Reproduce research from paper "Predicting the direction of stock market prices using random forest"
Roffildlibrary
Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS
Edarf
exploratory data analysis using random forests
Stock Market Sentiment Analysis
Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
Cnn Svm Classifier
Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
H2o 3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Thundergbm
ThunderGBM: Fast GBDTs and Random Forests on GPUs
Deep Forest
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
Grf
Generalized Random Forests
Pytorch classification
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
User Machine Learning Tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Rrcf
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
2020plus
Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests
AdaptiveRandomForest
Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04
EurekaTrees
Visualizes the Random Forest debug string from the MLLib in Spark using D3.js
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