Showing 5 of total 5 results (show query)
thomasp85
lime:Local Interpretable Model-Agnostic Explanations
When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <arXiv:1602.04938>.
Maintained by Emil Hvitfeldt. Last updated 3 years ago.
caretmodel-checkingmodel-evaluationmodelingcpp
485 stars 11.07 score 732 scripts 1 dependents
norskregnesentral
shapr:Prediction Explanation with Dependence-Aware Shapley Values
Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.
Maintained by Martin Jullum. Last updated 2 days ago.
explainable-aiexplainable-mlrcpprcpparmadilloshapleyopenblascppopenmp
154 stars 10.59 score 175 scripts 1 dependents
radiant-rstats
radiant.model:Model Menu for Radiant: Business Analytics using R and Shiny
The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.
Maintained by Vincent Nijs. Last updated 6 months ago.
19 stars 6.18 score 80 scripts 2 dependents
bioc
ttgsea:Tokenizing Text of Gene Set Enrichment Analysis
Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichment
4.95 score 3 scripts 3 dependents
timcdlucas
disaggregation:Disaggregation Modelling
Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.
Maintained by Tim Lucas. Last updated 5 months ago.
2 stars 4.60 score 9 scripts