Showing 3 of total 3 results (show query)
majkamichal
naivebayes:High Performance Implementation of the Naive Bayes Algorithm
In this implementation of the Naive Bayes classifier following class conditional distributions are available: 'Bernoulli', 'Categorical', 'Gaussian', 'Poisson', 'Multinomial' and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.
Maintained by Michal Majka. Last updated 2 months ago.
classification-modeldatasciencemachine-learningnaive-bayes
37 stars 10.47 score 1.0k scripts 6 dependents
asalavaty
influential:Identification and Classification of the Most Influential Nodes
Contains functions for the classification and ranking of top candidate features, reconstruction of networks from adjacency matrices and data frames, analysis of the topology of the network and calculation of centrality measures, and identification of the most influential nodes. Also, a function is provided for running SIRIR model, which is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment of dependence and correlation of two network centrality measures as well as the conditional probability of deviation from their corresponding means in opposite direction. Fred Viole and David Nawrocki (2013, ISBN:1490523995). Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695. Adopted algorithms and sources are referenced in function document.
Maintained by Adrian Salavaty. Last updated 6 months ago.
centrality-measuresclassification-modelinfluence-rankingnetwork-analysispriaritization-model
27 stars 6.54 score 43 scripts 1 dependents
jared-fowler
prettyglm:Pretty Summaries of Generalized Linear Model Coefficients
One of the main advantages of using Generalised Linear Models is their interpretability. The goal of 'prettyglm' is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. 'prettyglm' helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. 'prettyglm' also creates beautiful relativity plots for categorical, continuous and splined coefficients.
Maintained by Jared Fowler. Last updated 1 years ago.
classificationclassification-modeldata-sciencedata-visualizationglmlinear-modelsregressionregression-analysisregression-modelregression-modelsstatistical-models
3 stars 4.73 score 36 scripts