Presented at PyData LA, on Dec 5th 2019
Description: "Choosing the right evaluation metric for your machine learning project is crucial, as it decides which model you’ll ultimately use. How do you choose an appropriate metric? This talk will explore the important evaluation metrics used in regression and classification tasks, their pros and cons, and how to make a smart decision."
Links from the last slide:
scikit-learn User Guide: https://scikit-learn.org/stable/user_guide.html
"Root mean square error (RMSE) or mean absolute error (MAE)?" by Tianfeng Chai, R. R. Draxler: https://www.researchgate.net/publication/262980567_Root_mean_square_error_RMSE_or_mean_absolute_error_MAE
Tip 8 from "Ten quick tips for machine learning in computational biology": https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3
“Macro- and micro-averaged evaluation measures” by Vincent Van Asch : https://pdfs.semanticscholar.org/1d10/6a2730801b6210a67f7622e4d192bb309303.pdf
Blog post versions of the talk: mkhalusova.github.io