Displaying 1 to 8 from 8 results

Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library. PHP-ML requires PHP >= 7.1.

machine-learning classification cross-validation feature-extraction artificial-intelligence neural-network data-scienceThe resamplr package provides functions that implement resampling methods including the bootstrap, jackknife, random test/train sets, k-fold cross-validation, leave-one-out and leave-p-out cross-validation, time-series cross validation, time-series k-fold cross validation, permutations, rolling windows. These functions generate data frames with resample objects that work with the modelling pipeline of modelr and the tidyverse. The resamplr package includes functions to generate data frames of lazy resample objects, as introduced in the tidyverse modelr package. The resample class stores the a "pointer" to the original dataset and a vector of row indices. The object can be coerced to a dataframe with as.data.frame and the row indices with as.integer.

cross-validation tidyverse permutation jackknife resampling-methods rolling-windows bootstrap modelrLeave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4. Online, arXiv preprint arXiv:1507.04544.

cross-validation stan r-package bayesian-data-analysis information-criterion bayesian-methods model-weightsneuropredict is part of a broader intiative to develop easy, comprehensice and standardized predictive analysis. See here for an overview and the bigger picture idea.

neuroimaging machine-learning structural-imaging anatomical-mri cross-validation functional-connectivity tractography tract-based-statistics resting-state pattern-recognition easy-to-use reportA guide to using SuperLearner for prediction. Also many Coursera offerings and other online classes.

superlearner cross-validation statistical-learning ensembles tmle targeted-learningThe cvAUC R package provides a computationally efficient means of estimating confidence intervals (or variance) of cross-validated Area Under the ROC Curve (AUC) estimates. In binary classification problems, the AUC is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance.

cross-validation confidence-intervals auc machine-learning statistics r varianceThe subsemble package is an R implementation of the Subsemble algorithm. Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. Stephanie Sapp, Mark J. van der Laan & John Canny. Subsemble: An ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6):1247-1259, 2014.

ensemble ensemble-learning cross-validation machine-learning machine-learning-algorithms r big-dataPort of to port libsvm v3.22 using emscripten , for usage in the browser or nodejs. 2 build targets: asm and WebAssembly. What is libsvm? libsvm is a c++ library developped by Chih-Chung Chang and Chih-Jen Lin that allows to do support vector machine (aka SVM) classification and regression.

svm libsvm cross-validation emscripten support-vector-machine regression classification machine learning support vector machines
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**