So I was thinking like this - if you have a data set with 200000 variables and 70,000 examples how would you find a model without using a search process? On the other hand if you have 20 variables and or if you understand which of the 200000 variables are the ones to worry about then you can build a model by hand. I guess that also statisticians are working to summarise or create insight about data while ML is working to create a prediction (although statistical models can be used to do that too).
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So I was thinking like this - if you have a data set with 200000 variables and 70,000 examples how would you find a model without using a search process? On the other hand if you have 20 variables and or if you understand which of the 200000 variables are the ones to worry about then you can build a model by hand. I guess that also statisticians are working to summarise or create insight about data while ML is working to create a prediction (although statistical models can be used to do that too).
Why do you think that statisticians only build models by hand? Dimension reduction techniques are employed in statistical learning as well.