Search Results for "roc"
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Showing 18 open source projects for "roc"
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing the model structure, displaying the data sample, visualizing the relationship between hyperparameters and model metrics, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more. ... -
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Criterial
The package for statistical data analysis and applied mathematics
Putting truth before show-off. Criterial is an add-in for desktop versions of LibreOffice Calc (and forks) for statistical data analysis. The project is built on the refined expertise and core concepts of the AtteStat and StatAnt projects. Completely free. No donations required. Comes with absolutely no warranty. Rating: Everyone (All ages). MEDICAL DISCLAIMER: This software is not certified for use in healthcare and should not be used for diagnostic or treatment purposes. NOTICE FOR EU... -
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mlr
Machine Learning in R
R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods,... -
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Machine Learning Cheat Sheet
Classical equations and diagrams in machine learning
This repository is a visually rich and well-organized “cheat sheet” summarizing core machine learning concepts, algorithms, formulas, and best practices. It includes summaries of supervised and unsupervised learning methods, model evaluation metrics (accuracy, precision, recall, ROC/AUC), overfitting/underfitting, regularization (L1/L2), cross-validation, feature engineering techniques, and perhaps tips for hyperparameter tuning. Each section is presented concisely, often with diagrams, formula snippets, and short explanatory notes to serve as quick reference for students, practitioners, or interview prep. The repository is ideal for those who want a compact, at-a-glance reminder of ML fundamentals without diving back into textbooks. ... -
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
...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. Server online at https://cbbio.online/AxPEP -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
...The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. ... -
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Technical indicators in Python
Technical indicators in Python
Technical indicators in Python For now there are: RSI - Relative Strength Index, SMA - Simple Moving Average, WMA - Weighted Moving Average, EMA - Exponential Moving Average, BB - Bollinger Bands, Bollinger Bandwidth, %B, ROC and MA envelopes When I can I will add more. If anyone wishes to contribute with new code or corrections/suggestions, feel free. -
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EER
A WEKA package to calculate the Equal Error Rate (EER).
Calculates the Equal Error Rate (EER) using the ROC curve. This error is very used in Biometrics. This error is calculated when False Accept Error and False Rejecti Error are equal. This is a package for WEKA. -
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ROC(Taiwan) Judgement Search
ROC Judgement Search 中華民國司法裁判書查詢與下載
ROC(Taiwan) Judgement Search and Download 中華民國司法判決書搜尋與下載，能夠快速的查詢某一區段或是某一案件類型之判決文，供下載該區段判決文以供研究司法狀態或是判決型態。其他搜尋問題請參照:http://jirs.judicial.gov.tw/FJUD/TextHelp.htm -
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ARDEN
Specificity Control for Read Alignments Using an Artificial Reference
We introduce ARDEN (Artificial Reference Driven Estimation of false positives in NGS data), a novel benchmark that estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It allows the computation of error rates specifically for a dataset and the construction of a ROC-curve. Thereby, it can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation. -
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XMLRPC for C#
Lightweight XMLRPC for C#
Lightweight XMLRPC implementation for C#. Works fine with MonoTouch and Mono for Android (www.xamarin.com) because reflection.emit is not used! -
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GeneNetWeaver (GNW) is an open-source tool for in silico benchmark generation and performance profiling of network inference methods. GNW was used to generate the community-wide DREAM3, DREAM4 and DREAM5 In Silico Challenges.
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Game Stats
Logfile parser for Call of Duty (COD) and Soldier of Fortune 2
Game Stats generates web-based static .HTML statistics by parsing a game log file. Currently supports COD (Call of Duty) and Soldier of Fortune 2 w/OSP or ROC mods. As of 1.4, it ships with its own executable, and doesn't require PHP or MySQL. -
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Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
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ROC.KIT is an application for health sciences that automatically calculates ROC curves (receiver operating charateristic plots) from continuous medical data like laboratory results.
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Roc is a J2EE/Ajax framework for enterprise rapid devlopment. It makes you to be free from transaction management, remote calling program ,ajax calling program and so on. And it is easy to learn and use.
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Roc's purpose is to serve as a tool for those who know little about programming. It strives to create RPGs similar to those on your video game console. Roc is written in Python and wxPython. It will support all platforms compatible with wxPython.
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