Search Results for "random forest"
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Showing 28 open source projects for "random forest"
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Random Cut Forest by AWS
An implementation of the Random Cut Forest data structure
This repository contains implementations of the Random Cut Forest (RCF) probabilistic data structure. RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for streaming data. Later new algorithms based on RCFs were developed for density estimation, imputation, and forecasting. The different directories correspond to equivalent implementations in different languages, and bindings to to those base implementations, using language-specific features for greater flexibility of use. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
...To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling. -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their... -
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Kaata Zero (Tic tac toe)
Tic-Tac-Toe game for Windows (also known as Kaata Zero)
.... ### Game modes - **Player vs Computer** — You play **X**, the computer plays **O** - **2 Player** — Two people take turns on the same keyboard and screen ### AI difficulty - **Easy** — Random moves - **Medium** — Mix of tactical and random play - **Hard** — Minimax strategy (strong opponent) ### Scoring - Easy win: **+50** points - Medium win: **+100** points - Hard win: **+200** points - Draw: **−50** points - Loss: score resets to **0** ### What makes it enjoyable - Five color **skins**: Classic, Midnight, Ocean, Forest, Cand -
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DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
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HealthFusion
AI Disease Detections System
...HealthFusion is a user-friendly app that can be accessed from the comfort of homes, making it accessible to everyone. The use of advanced technologies such as Convolutional Neural Networks, Random Forest, and XGBoost allows for accurate and timely detection of diseases, leading to better patient outcomes. -
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rafah
Random Forest Assignment of Hosts
One fundamental question when trying to describe viruses of Bacteria and Archaea is: Which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), which outperformed other methods for virus-host prediction. Our rationale was that the machine could learn the associations between genes and hosts much more efficiently than a human, while also using the information contained in the hypothetical proteins. Random forest models were built using the Ranger package in R. -
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SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
...By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. Several algorithms are used during model selection, including Random Forest, Extra Trees, AdaBoost, Gradient Boosting, and Support Vector Machines. The project evaluates models by predicting price direction within very short time windows and then applying a simple trading strategy based on those predictions. It also measures profitability through profit-and-loss analysis derived from the predicted signals. -
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Splicing Prediction Pipeline
Splicing Prediction Pipeline or SPiP
...You can also download SPiP in zip format: https://sourceforge.net/projects/splicing-prediction-pipeline/files/SPiP_alone_v2_1.zip For Linux (GitHub): https://github.com/LBGC-CFB/SPiP If you have questions, please contact me to: r.leman@baclesse.unicancer.fr or raphael.leman@orange.fr v2.1 corrected (07/2021) : correction of score calculation v2.1 (06/2021): Most strigent on "Alter by complex event" v2.0 (04/2021): New SPiP with random forest modelization old change detailled in https://sourceforge.net/projects/splicing-prediction-pipeline/files/changeLogBeforev2.0.txt -
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
...However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3 data ratio achieved a very high accuracy of 96%, MCC of 0.9, AUC-ROC of 0.99 and Kappa statistic of 0.9. AmPEP outperforms existing methods with respect to accuracy, MCC, and AUC-ROC when tested using the benchmark datasets. ... -
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miniABS
miniABS (mini Absolute Breast Cancer Subtyper)
miniABS (mini Absolute Breast Cancer Subtyper) is an absolute, single-sample subtype classifier for breast cancer using Random Forest model of pairwise gene expression ratios (PGER) among 11 functional genes. With a systematic gene selection and reduction step, we aimed to minimize the size of gene set without losing a functional interpretability of the classifier. We validated the model performance using a large, heterogeneous cohort that consists of multiple public datasets across four different platforms. ... -
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benchm-ml
A benchmark of commonly used open source implementations
...It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to... -
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This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. -
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A machine learning-based method for somatic variant identification from low-pass next-generation sequencing.
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Kontrol49's AutoM8
Automates Steves movement in 8 directions.
...Nobody likes to see Steve standing still. When we take a break from playing, wouldn't it be nice to give Steve something to do? ...Now you can. With Kontrol49's AutoM8, you can have Steve move a random amount of blocks, in any of 8 random directions. When Steve stops moving, you can even have him place an item. Is Steve an Artist? Give him a stack of torches and let him randomly place them. Got a SuperFlat world that needs a forest? You could plant every sapling by hand, or, let Steve take care of it. ...and yes, this is an anti-AntiAFK movement generator that actually works. ... -
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Red-RF
Reduced Random Forest for big data
Red(uced)-RF, a new type of Random Forests that adopts dynamic data reduction and weighted upvoting techniques. Red-RF is favorably applicable to big data: it demonstrates an accurate and efficient performance while achieving a considerable data reduction w.r.t. dataset size. Manuscripts available on IEEE Xplore: H. Mohsen, H. Kurban, K. Zimmer, M. Jenne and M. Dalkilic. Red-RF: Reduced Random Forests using priority voting & dynamic data reduction. In IEEE BigData Congress'2015. H.... -
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forest chung is a free html5 webgl 3D fps / horse riding hack & slash online exploration / fighting game written in javascript with shaders and vaste random procedural world, trees, grass, rocs, water, monsters, woman riders .
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Unsupervised Random Forest
On-line Unsupervised Random Forest
This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. ... -
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FP-RF
A fuzzy pattern – random forest procedure for feature selection
A method for feature selection and prioritization aiming at generating robust and stable sets of features with high predictive power. This method uses the fuzzy logic for a first unbiased informative feature selection process and a modified version of the Random Forest to prioritize the candidate discriminant features. -
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TRF_Pathway
A Random Forest based Pathway association analysis tool
The TRF-pathway package implements the powerful two-stage random forest based pathway analysis. The manuscript discussing the method is currently under revision at PLoS One. -
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FACIL
FACIL: Fast and Accurate genetic Code Inference and Logo
FACIL infers the genetic code directly from any set of nucleic acid sequences and assigns a Random Forest-based reliability score to its predictions. Please cite: BE Dutilh et al. (2011), "FACIL: fast and accurate genetic code inference and logo", Bioinformatics 27: 1929. http://www.ncbi.nlm.nih.gov/pubmed/21653513 -
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...New Features Include: -All the Features of the 3.7.3 Weka Package -Multi-Threaded ensemble learning -An enhancement on the popular RandomForest Learner based on "Dynamic Integration with Random Forests" by Tsymbal et al. 2006 and "Improving Random Forests" by Robnik-Sikonja 2004. -More enhancements to the voting mechanisms in Random Forest -Possibility to output Feature Weights according to the original Breiman Paper 2001
