|
Function calculates mean squared error as predicted vs. observed
● Data Source:
CranContrib
● Keywords: arith, math
● Alias: MSE
●
0 images
|
|
This function limits elapsed time spent on single model development. It uses low-level functions of parallel packege and sets the fork process with time limit. If the result is not returned within set time, it kills fork. Function shouldn't be called from R console. The function is not used under Windows OS. Only Unix-like systems have fork functionality.
● Data Source:
CranContrib
● Keywords: error, methods
● Alias: timeout
●
0 images
|
|
The function uses the caret package advantage to perform fitting of numerous regression models.
● Data Source:
CranContrib
● Keywords: models, robust
● Alias: regVarImp
●
0 images
|
|
Function installs the packages that are listed in data(requiredPackages). The function is called within fscaret function. If argument "installReqPckg = TRUE" the function installs required packages.
● Data Source:
CranContrib
● Keywords: data, package
● Alias: installPckg
●
0 images
|
This package provide fast and automated feature selection based on caret package modeling methods. The main advantage of this extension is that it requires minimum user involvement. Also the variety of used methods in combination with the scaling according to RMSE or MSE obtained from models profit the user. The idea is based on the assumption that the variety of models will balance the roughness of calculations (default model settings are applied). On Windows OS the time limiting function is off, multicore functionalaity is enabled via parLapply() function of package 'parallel'. Acknowledgments:
This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012
● Data Source:
CranContrib
● Keywords: package
● Alias: fscaret-package
●
0 images
|
|
impCalc function is designed to scale variable importance according to MSE and RMSE calculations. It also stores the raw MSE, RMSE, F-measure and developed models if saveModel=TRUE. impCalc is low-level function, it shouldn't be used alone unless user has trained models from caret package stored in RData files.
● Data Source:
CranContrib
● Keywords: design, models
● Alias: impCalc
●
0 images
|
|
The functionality is realized in two main steps:
● Data Source:
CranContrib
● Keywords: robust, univar
● Alias: dataPreprocess
●
0 images
|
|
The function uses the caret package advantage to perform fitting of numerous classification models.
● Data Source:
CranContrib
● Keywords: models, robust
● Alias: classVarImp
●
0 images
|
|
Secondary function imputes the mean to columns with NA data.
● Data Source:
CranContrib
● Keywords: logic, math
● Alias: impute.mean
●
0 images
|
|
Main function for fast feature selection. It utilizes other functions as regPredImp or impCalc to obtain results in a list of data frames.
● Data Source:
CranContrib
● Keywords: array, iteration, methods, optimize
● Alias: fscaret
●
0 images
|
providing 
.