Showing 6 of total 6 results (show query)
thie1e
cutpointr:Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks
Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.
Maintained by Christian Thiele. Last updated 4 months ago.
bootstrappingcutpoint-optimizationroc-curvecpp
88 stars 10.44 score 322 scripts 1 dependents
bioc
iCOBRA:Comparison and Visualization of Ranking and Assignment Methods
This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results.
Maintained by Charlotte Soneson. Last updated 3 months ago.
14 stars 8.86 score 192 scripts 1 dependents
brian-j-smith
MachineShop:Machine Learning Models and Tools
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Maintained by Brian J Smith. Last updated 7 months ago.
classification-modelsmachine-learningpredictive-modelingregression-modelssurvival-models
62 stars 7.95 score 121 scriptsbioc
kebabs:Kernel-Based Analysis of Biological Sequences
The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions.
Maintained by Ulrich Bodenhofer. Last updated 5 months ago.
supportvectormachineclassificationclusteringregressioncpp
6.58 score 47 scripts 3 dependents
serkor1
SLmetrics:Machine Learning Performance Evaluation on Steroids
Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in 'C++' for scalability and efficiency.
Maintained by Serkan Korkmaz. Last updated 2 days ago.
cppdata-analysisdata-scienceeigen3machine-learningperformance-metricsrcpprcppeigenstatisticssupervised-learningcppopenmp
22 stars 6.56 score
doebler
mada:Meta-Analysis of Diagnostic Accuracy
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Maintained by Philipp Doebler. Last updated 3 years ago.
2 stars 5.09 score 58 scripts 3 dependents