illdoit2morrow wrote: »
My question I suppose is, does one really need to know how the algorithms work, or is a good understanding of linear regression, clustering, classification etc and their associated algorithms good enough?
Nelbert wrote: »
As with most things it depends!
If you present to someone with stats knowledge or even an inquisitive nature and a head for maths you may lose credibility if you can’t explain sampling, distributions and for example how boosting and bagging work with algorithms like xgboost and random forest (let alone neural networks and the joys of explaining how they work to a non maths audience....)
Working with sample datasets and testing (repeatedly) different parameters will get you a better understanding of how things work and their impact.
Decision trees are great as when visualised they become fairly self evident to most business audiences but you don’t want good work ruined by a probing question you can’t answer.
I managed to explain (broadly) how a support vector machine worked to someone who “hates maths” with two simple scatterplot type diagrams I scrawled on a whiteboard. I could see at least 3 other people in the room who looked relieved after the 30ish second explanation.
Once you’ve built up the credibility some audiences will ask less questions because you know your stuff, others will ask more for the exact same reason (and they are curious as to the “how”).