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
Teradata tbl object.
Named list member can be referenced directly with the "$" operator
using name: result.
Examples
# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("text_classifier_trainer_example", "texttrainer_input") loadExampleData("text_classifier_example", "textclassifier_input") # Create remote tibble objects. texttrainer_input <- tbl(con, "texttrainer_input") textclassifier_input <- tbl(con, "textclassifier_input") # The model file "knn.bin" generated by "td_text_classifier_trainer_mle" # function is used by "td_text_classifier_mle" to classify the input text. 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') 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. td_text_classifier_evaluator_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')