4 Reasons why an Accurate Analytics Model may not be Good Enough

Here are four reasons why the result of your analytics modeling might be correct (according to some accuracy metric), but it might not be the right answer:

  1. Your model may be underfit.

  2. Your model may be overfit.

  3. Your model may be biased.

  4. Your model may be suffering from the false positive paradox.

In data science, we are trained to keep searching even after we find a model that appears to be accurate. Data Scientists should continue searching for a better solution, for at least the four reasons listed above. Please note that I am not advocating “paralysis of analysis”, where never-ending searches for new and better solutions are just an excuse (or a behavior pattern) that prevents one from making a final decision. Good leaders know when an answer is “good enough”. We discussed this in a previous article: “Machine Unlearning – The Value of Imperfect Models”…

(For more discussion of the four cases listed above, continue reading herehttps://www.mapr.com/blog/4-reasons-look-further-accurate-answer-your-analytics-question)

Follow Kirk Borne on Twitter @KirkDBorne

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