Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm

PONE-D-20-24775

Dear Dr. Asahina,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: With increasing use of classifiers to identify behaviours, a careful analysis of their limitations and possible biases is important. In this paper the authors apply a widely used classifier (JAABA) to three fly behaviours and compare the results to human annotators. They analyse the agreement between human annotators and the automated classifier, show that human confidence is somewhat predictive of automated classifier performance (behaviours that humans are less certain about are more likely to be misclassified by the automated classifier), and investigate how the classifier performance changes with differences in training data and tracking parameters. The paper is thorough, clearly written, and appropriately analysed.

In my opinion it can be published as is.

The data to reproduce the plots and analysis is included in the supplementary table but the video files used to train the classifiers (and the trained classifiers themselves) are not currently available. If possible, it would be useful to share these as well using something like zenodo.org or a similar repository.

One very minor improvement: the equations for precision and recall could be better formatted as equations rather than text to make them more readable.

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Reviewer #1: No