Feature Model Select {qeML}

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)