Research DataOpen Access

Validation of the performance of the random forest model for activity classification

Description

We applied the trained random forest models to the 50% data withheld for testing to evaluate its performance in classifying bat activity. Similarly, we applied random forest models to the bird and human activity dataset after calculating the same predictor variables as for the bats. We first calculated the true positive rate (TPR) as the ratio of correctly identified incidents by comparing the observed data with the activity class attributed by the trained random forest models for all dataset types (i.e., human activity dataset, woodpecker, and bat video sequences). Next, we calculated the models’ F-score (F1) using the Caret package (Kuhn, 2008). Data and code are stored here.

Metadata

show more
Files
Document
Type
Size

Creative Commons license

Except where otherwised noted, this item's license is described as Creative Commons Attribution 4.0

Version History

Now showing 1 - 2 of 2
VersionDateSummary
2*
2022-08-05 13:18:51
2022-03-04 08:20:59
* Selected version