105 Open source projects that are alternatives of or similar to Randomforest

Generative Forests in Python

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

A curated list of gradient boosting research papers with implementations.

Python code for common Machine Learning Algorithms

Predicting the winner of 2019 cricket world cup using random forest algorithm

A curated list of data mining papers about fraud detection.

This 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".

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset

Generalized Random Forests

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Efficient Online Transfer Learning for 3D Object Detection in Autonomous Driving

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

文本分类基准测试

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Online machine learning methods

exploratory data analysis using random forests

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

Machine Learning library for the web and Node.

Machine learning for C# .Net

Jupyter Notebooks and miscellaneous

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Machine Learning Lectures at the European Space Agency (ESA) in 2018

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)

Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

A python library to build Model Trees with Linear Models at the leaves.

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

simple tutorial on Machine Learning with Scikitlearn

🏭 JavaScript Machine Learning Toolkit

Visualizes the Random Forest debug string from the MLLib in Spark using D3.js

This is the implementation of Sparse Projection Oblique Randomer Forest

R package "missRanger" for fast imputation of missing values by random forests.

gesture recognition toolkit

A Unified Framework for Random Forest Prediction Error Estimation

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

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.

Machine Learning University: Decision Trees and Ensemble Methods

Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS

This 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…

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

Solution of the Titanic Kaggle competition

A python library for decision tree visualization and model interpretation.

Master's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

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

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

Handwritten Digit Recognition using Machine Learning and Deep Learning

A New, Interactive Approach to Learning Data Science

Text Classification Algorithms: A Survey

I will update this repository to learn Machine learning with python with statistics content and materials

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html