creates a summary with ensemble coverage and precision.

Description

Creates a summary with ensemble coverage and precision values for an ensemble greater than the threshold specified.

Usage

create_ensembleSummary(document_summary)

Arguments

Details

This summary is created in the create_analytics function. Note that a threshold value of 3 will return ensemble coverage and precision statistics for topic codes that had 3 or more (i.e. >=3) algorithms agree on the same topic code.

Author(s)

Loren Collingwood <lorenc2@uw.edu>, Timothy P. Jurka <tpjurka@ucdavis.edu>

Examples

library(RTextTools)
data(NYTimes)
data <- NYTimes[sample(1:3100,size=100,replace=FALSE),]
matrix <- create_matrix(cbind(data["Title"],data["Subject"]), language="english", 
removeNumbers=TRUE, stemWords=FALSE, weighting=tm::weightTfIdf)
container <- create_container(matrix,data$Topic.Code,trainSize=1:75, testSize=76:100, 
virgin=FALSE)
models <- train_models(container, algorithms=c("MAXENT","SVM"))
results <- classify_models(container, models)
analytics <- create_analytics(container, results)

ensemble <- create_ensembleSummary(analytics@document_summary)
ensemble

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(RTextTools)
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RTextTools/create_ensembleSummary.Rd_%03d_medium.png", width=480, height=480)
> ### Name: create_ensembleSummary
> ### Title: creates a summary with ensemble coverage and precision.
> ### Aliases: create_ensembleSummary
> ### Keywords: method
> 
> ### ** Examples
> 
> library(RTextTools)
> data(NYTimes)
> data <- NYTimes[sample(1:3100,size=100,replace=FALSE),]
> matrix <- create_matrix(cbind(data["Title"],data["Subject"]), language="english", 
+ removeNumbers=TRUE, stemWords=FALSE, weighting=tm::weightTfIdf)
> container <- create_container(matrix,data$Topic.Code,trainSize=1:75, testSize=76:100, 
+ virgin=FALSE)
> models <- train_models(container, algorithms=c("MAXENT","SVM"))
> results <- classify_models(container, models)
> analytics <- create_analytics(container, results)
> 
> ensemble <- create_ensembleSummary(analytics@document_summary)
> ensemble
       n-ENSEMBLE COVERAGE n-ENSEMBLE RECALL
n >= 1                1.00              0.32
n >= 2                0.24              0.33
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>