MusicCityTech, September 7th.
Nashville, TN
Choosing the right evaluation metric for your machine learning project is crucial, as it decides which model you’ll ultimately use. Those coming to ML from software development are often self-taught, but practice exercises and competitions generally dictate the evaluation metric. In a real-world scenario, 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:
https://scikit-learn.org/stable/user_guide.html https://www.researchgate.net/publication/262980567_Root_mean_square_error_RMSE_or_mean_absolute_error_MAE
https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3
https://pdfs.semanticscholar.org/1d10/6a2730801b6210a67f7622e4d192bb309303.pdf
http://mkhalusova.github.io