Given that many data people run across is tabular, I appreciate your advice about the importance of statistics. Also kudos for mentioning hypothesis testing (no one in this thread mentioned it). Lastly, I’d add that ML practitioners will gain a lot by listening to statisticians and economists on the issue of data quality, e.g. selection bias.
That said, I am not as cynical about “machine learning.” ML and “data science” brought the importance of prediction front and center, i.e. can you fit a model that accurately predict the target value given a previously seen input? This point is made by the recently published stats textbook Computer Age Statistical Inference (Efron and Hastie).
In some applications, it may be beneficial to choose black box models with high predictive accuracy, as the goal for these applications is prediction, not interpreting individual model coefficients.
That said, I am not as cynical about “machine learning.” ML and “data science” brought the importance of prediction front and center, i.e. can you fit a model that accurately predict the target value given a previously seen input? This point is made by the recently published stats textbook Computer Age Statistical Inference (Efron and Hastie).
In some applications, it may be beneficial to choose black box models with high predictive accuracy, as the goal for these applications is prediction, not interpreting individual model coefficients.