docs/00-manuscripts/mdpi/reviews/review1/reviewer4.md

In pat-s/2019-feature-selection: Research Compendium package for the publication "Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?"

Reviewer 4

In the manuscript, “Monitoring Forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?”, the authors described a study for the analysis of tree defoliation in the northern region of Spain, and this using hyperspectral data as input for machine learning which used hyperparameter tuning and filter-based feature selection. Subsequently, a comparison of the performance of the considered machine learning models. The main objective of this study is to demonstrate whether expert-based or data-driven feature engineering has a positive influence on model performance.

The manuscript is overall quite well written and well organized, although I find that the style of the latter needs to be improved to make it easier to read and understand. The study discussed is fairly well detailed and well presented. Therefore, I recommend for publication.

Thanks for the positive feedback and for reviewing the manuscript!

pat-s/2019-feature-selection documentation built on Dec. 24, 2021, 8:40 a.m.