ModelDev function

Feature Selection and Model Building

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

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

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)

Author(s)

Norm Matloff