Of the 10% that can't be solved that way, 90% are solved with data cleaning + a linear model.
Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
For the remaining .1%, sure, deep learning I guess.
> not fucking up your data, modeling your tables correctly, and a SQL query
And getting a pony.
> Of the 10% that can't be solved that way, 90% are solved with data cleaning + a linear model.
87% of statistics are made up.
> Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
But why?
This reminds me of how some people in the early '80s sneered at people who did their calculations using computers - recommending instead to memorise a billion mathematical shortcuts that would take longer to learn than programming a computer.
> 87% of statistics are made up.
Yes, of course, I didn't mean "lower integer part of nine tenths of the total number of problems". Did that really need to be specified?
>> Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
> But why?
Is it really controversial that you should go for the simplest model that works?
I guarantee that 90% of the things I want to do have nothing to do with a table lookup.
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