close

Showing 4 of total 4 results (show query)

jgraux

PRROC:Precision-Recall and ROC Curves for Weighted and Unweighted Data

Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.

Maintained by Jan Grau. Last updated 12 days ago.

8.35 score 1.2k scripts 56 dependents

veseshan

clinfun:Clinical Trial Design and Data Analysis Functions

Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles.

Maintained by Venkatraman E. Seshan. Last updated 1 years ago.

fortran

5 stars 7.86 score 124 scripts 8 dependents

nicolalunardon

ROSE:Random Over-Sampling Examples

Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.

Maintained by Nicola Lunardon. Last updated 4 years ago.

4 stars 6.86 score 1.6k scripts 3 dependents

cran

randomUniformForest:Random Uniform Forests for Classification, Regression and Unsupervised Learning

Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.

Maintained by Saip Ciss. Last updated 3 years ago.

cpp

4 stars 2.90 score

: