#Random-forest
Showing 60 of 106 repositories tagged #random-forest, ranked by stars
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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.
π :bar_chart: :bulb: Orange: Interactive data analysis
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A python library for decision tree visualization and model interpretation.
A collection of research papers on decision, classification and regression trees with implementations.
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.).
Text Classification Algorithms: A Survey
A curated list of data mining papers about fraud detection.
A curated list of Best Artificial Intelligence Resources
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
gesture recognition toolkit
Machine Learning inference engine for Microcontrollers and Embedded devices
ThunderGBM: Fast GBDTs and Random Forests on GPUs
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Impress your boss with interactive Decision Tree visualization
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Fast SHAP value computation for interpreting tree-based models
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
Machine Learning library for the web and Node.
Compendium of free ML reading resources
π² Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
Multiple Imputation with LightGBM in Python
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Machine Learning Lectures at the European Space Agency (ESA) in 2018
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
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
An end-to-end machine learning and data mining framework on Hadoop
Quantile Regression Forests compatible with scikit-learn.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
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This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
Anomaly based Malware Detection using Machine Learning (PE and URL)
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
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)
YouTube Like Count Predictions using Machine Learning
A machine learning malware analysis framework for Android apps.
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Solution of the Titanic Kaggle competition
Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS
Beta Machine Learning Toolkit
Python library for backtesting technical/mechanical strategies in the stock and currency markets
Repository For Codes And Concept Taught in Udemy Course
Machine Learning Algorithms on NSL-KDD dataset
Credit scoring machine learning algorithm which predicts probability of default
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.
Scikit-learn compatible decision trees beyond those offered in scikit-learn
A lite chrome extension for detecting phishing sites using random forest classifier
Includes top ten must know machine learning methods with R.
Malware Classification using Machine learning
Data Science, Machine Learning, Deep Learning, NLP, Python & Library's cheat Sheet - Interview Questions & Notes
ExeRay AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.
I will update this repository to learn Machine learning with python with statistics content and materials
Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.