ModelDev: Feature Selection and Model Building

In matloff/qeML: Quick and Easy Machine Learning Tools

Feature Selection and Model Building

Description

Utilties to help build models, both in specific applications such as time series and text analysis, and in general tools..

Usage

qeCompare(data,yName,qeFtnList,nReps,opts=NULL,seed=9999)
qeFT(data,yName,qeftn,pars,nCombs,nTst,nXval,showProgress=TRUE)
qeText(data,yName,kTop=50,stopWords=tm::stopwords("english"),
   qeName,opts=NULL,holdout=floor(min(1000,0.1*nrow(data))))
qeTS(lag,data,qeName,opts=NULL,holdout=floor(min(1000,0.1*length(data))))
## S3 method for class 'qeText'
predict(object,newDocs,...)
## S3 method for class 'qeTS'
predict(object,newx,...)

Arguments

...	Further arguments.
object	Object returned by a qe-series function.
newx	New data to be predicted.
newDocs	Vector of new documents to be predicted.
lag	number of recent values to use in predicting the next.
qeName	Name of qe-series predictive function, e.g. 'qeRF'.
stopWords	Stop lists to use.
nTst	Number of parameter combinations.
kTop	Number of most-frequent words to use.
data	Dataframe, training set. Classification case is signaled via labels column being an R factor.
yName	Name of the class labels column.
holdout	If not NULL, form a holdout set of the specified size. After fitting to the remaining data, evaluate accuracy on the test set.
qeFtnList	Character vector of qe* function names.
nReps	Number of holdout sets to generate.
opts	R list of optional arguments for none, some or all of th functions in qeFtnList.
seed	Seed for random number generation.
qeftn	Quoted string, specifying the name of a qe-series machine learning method.
pars	R list of hyperparameter ranges. See regtools::fineTuning.
nCombs	Number of hyperparameter combinations to run. See regtools::fineTuning.
nXval	Number of cross-validations to run. See regtools::fineTuning.
showProgress	If TRUE, show results as they arise. See regtools::fineTuning.

Details

Overviews of the functions:

• qeTs is a tool for time series modeling

• qeText is a tool for textual modeling

• qeCompare facilitates comparison among models

• qeFT does a random grid search for optimal hyperparameter values

Author(s)

Norm Matloff

Examples

data(mlb1)
# predict Weight in the mlb1 dataset, using qeKNN, with k = 5 and 25,
# with 10 cross-validations
qeFT(mlb1,'Weight','qeKNN',list(k=c(5,25)),nTst=100,nXval=10)

matloff/qeML documentation built on Dec. 15, 2024, 10:15 a.m.