Overview

File R-NumericEnsembles.spec of Package R-NumericEnsembles

# Automatically generated by CRAN2OBS
# 
# Spec file for package NumericEnsembles 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
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%global packname  NumericEnsembles 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.10.3 
Release:        0 
Summary:        Automatically Runs 18 Individual and 14 Ensembles of Models 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         NumericEnsembles_0.10.3.tar.gz 
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Suggests:	R-knitr
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Suggests:	R-testthat
%description 
Automatically runs 18 individual models and 14 ensembles on numeric 
data, for a total of 32 models. The package automatically returns 
complete results on all 32 models, 30 charts and six tables. The user 
simply provides the tidy data, and answers a few questions (for 
example, how many times would you like to resample the data). From 
there the package randomly splits the data into train, test and 
validation sets, fits each of models on the training data, makes 
predictions on the test and validation sets, measures root mean squared 
error (RMSE), removes features above a user-set level of Variance 
Inflation Factor, and has several optional features including scaling 
all numeric data, four different ways to handle strings in the data. 
Perhaps the most significant feature is the package's ability to make 
predictions using the 32 pre trained models on totally new (untrained) 
data if the user selects that feature. This feature alone represents a 
very effective solution to the issue of reproducibility of models in 
data science. The package can also randomly resample the data as many 
times as the user sets, thus giving more accurate results than a single 
run. The graphs provide many results that are not typically found. For 
example, the package automatically calculates the Kolmogorov-Smirnov 
test for each of the 32 models and plots a bar chart of the results, a 
bias bar chart of each of the 32 models, as well as several plots for 
exploratory data analysis (automatic histograms of the numeric data, 
automatic histograms of the numeric data). The package also 
automatically creates a summary report that can be both sorted and 
searched for each of the 32 models, including RMSE, bias, train RMSE, 
test RMSE, validation RMSE, overfitting and duration. The best results 
on the holdout data typically beat the best results in data science 
competitions and published results for the same data set. 
 
%prep 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
 
 
%build 
 
%install 
mkdir -p %{buildroot}%{rlibdir} 
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} 
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) 
rm -f %{buildroot}%{rlibdir}/R.css 
%fdupes -s %{buildroot}%{rlibdir} 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog
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