This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.
Incredibly useful to remind yourself of all the things you know but have forgotten in your frustration to fix it.
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