Description
The TextClassifierEvaluator function evaluates the precision,
recall, and F-measure of the trained model output by the
TextClassifier (td_text_classifier_mle
) function.
Usage
td_text_classifier_evaluator_mle (
object = NULL,
obs.column = NULL,
predict.column = NULL,
object.sequence.column = NULL,
object.order.column = NULL
)
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
obs.column |
Required Argument. |
predict.column |
Required Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class
"td_text_classifier_evaluator_mle" which is a named list containing
object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using the name: result.
Examples
# Get the current context/connection
con <- td_get_context()$connection
# Load example data.
loadExampleData("text_classifier_trainer_example", "texttrainer_input")
loadExampleData("text_classifier_example", "textclassifier_input")
# Create object(s) of class "tbl_teradata".
texttrainer_input <- tbl(con, "texttrainer_input")
textclassifier_input <- tbl(con, "textclassifier_input")
# The model file 'knn.bin' is generated by td_text_classifier_trainer_mle() function.
td_text_classifier_trainer_mle(data=texttrainer_input,
text.column='content',
category.column='category',
classifier.type='knn',
model.file='knn.bin',
classifier.parameters='compress:0.9',
nlp.parameters=c('useStem:true','stopwordsFile:stopwords.txt'),
feature.selection='DF:[0.1:0.99]',
data.sequence.column='id'
)
# The generated model file is used by td_text_classifier_mle() function to classify the
# input text.
td_text_classifier_out <- td_text_classifier_mle(newdata=textclassifier_input,
newdata.order.column='id',
text.column='content',
accumulate=c('id','category'),
model.file='knn.bin',
newdata.sequence.column='id'
)
# Example 1 - This example evaluates the precision, recall, and F-measure
# of the model output generated by td_text_classifier_mle() function.
text_classifier_eval_out <- td_text_classifier_evaluator_mle(object = td_text_classifier_out,
obs.column = 'category',
predict.column = 'out_category',
object.sequence.column = 'id',
object.order.column = 'id'
)