calculates the recall accuracy of the classified data.

Description

Given the true labels to compare to the labels predicted by the algorithms, calculates the recall accuracy of each algorithm.

Usage

recall_accuracy(true_labels, predicted_labels)

Arguments

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$Topic.Code,trainSize=1:75, testSize=76:100, 
virgin=FALSE)
models <- train_models(container, algorithms=c("MAXENT","SVM"))
results <- classify_models(container, models)
analytics <- create_analytics(container, results)
recall_accuracy(analytics@document_summary$MANUAL_CODE,
analytics@document_summary$GLMNET_LABEL)
recall_accuracy(analytics@document_summary$MANUAL_CODE,
analytics@document_summary$MAXENTROPY_LABEL)
recall_accuracy(analytics@document_summary$MANUAL_CODE,
analytics@document_summary$SVM_LABEL)

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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> 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/recall_accuracy.Rd_%03d_medium.png", width=480, height=480)
> ### Name: recall_accuracy
> ### Title: calculates the recall accuracy of the classified data.
> ### Aliases: recall_accuracy
> ### 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$Topic.Code,trainSize=1:75, testSize=76:100, 
+ virgin=FALSE)
> models <- train_models(container, algorithms=c("MAXENT","SVM"))
> results <- classify_models(container, models)
> analytics <- create_analytics(container, results)
> recall_accuracy(analytics@document_summary$MANUAL_CODE,
+ analytics@document_summary$GLMNET_LABEL)
[1] 0
> recall_accuracy(analytics@document_summary$MANUAL_CODE,
+ analytics@document_summary$MAXENTROPY_LABEL)
[1] 0
> recall_accuracy(analytics@document_summary$MANUAL_CODE,
+ analytics@document_summary$SVM_LABEL)
[1] 0
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>