Popular GitHub Repositories for Isolation Forests

Gradio WebUI for creators and developers, featuring key TTS (Edge-TTS, kokoro) and zero-shot Voice Cloning (E2E, F5-TTS, CosyVoice), with Whisper audio processing, RVC voice changer, YouTube download, UVR5 vocal isolation, and multilingual translation.

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.).

The AI toolkit for speech.

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.

Cross-platform speech toolset, used from the command-line or as a Node.js library. Includes a variety of engines for speech synthesis, speech recognition, forced alignment, speech translation, voice isolation, language detection and more.

A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.

Semantic LIDAR odometry and mapping for cylinderical objects (e.g. trees in forests)

Isolation Forest on Spark

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.

offical implementation of TKDE paper "Deep isolation forest for anomaly detection"

An Execution Isolation Architecture for LLM-Based Agentic Systems

Random Forests in Apache Spark

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

IoT intrusion Detection Model based on neural network and random forests

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

Security Analytics Engine - Anomaly Detection in Web Traffic

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.