## 7.2 Classical statistics vs. machine learning

• Two cultures of statistical analysis
• Data modeling vs. algorithmic modeling
• $$\approx$$ generative modelling vs. algorithmic modeling
• Generative modeling (classical statistics, Objective: Inference)
• Goal: understand how an outcome is related to inputs
• Analyst proposes a stochastic model that could have generated the data, and estimates the parameters of the model from the data
• Leads to simple and interpretable models BUT often ignores model uncertainty and out-of-sample performance
• Predictive modeling (Objective: Prediction)
• Goal: prediction, i.e., forecast the outcome for unseen (Q: ?) or future observations
• Analyst treats the underlying generative model for the data as unknown and considers the predictive accuracy of alternative models on new data.
• Leads to complex models that perform well out of sample BUT can produce black-box results that offer little insight on the mechanism linking the inputs to the output (but Interpretable ML)