
Episode 16: Data Science and Decision Making Under Uncertainty
Vanishing Gradients
Importance of Simplifying Models and Comparing to Basic Models
Decision-making involves modeling the real world, and humans excel in creating sophisticated mental models. Bayesian analysis emphasizes explicit assumptions. The biggest mistake in model building is complexity; simplicity is crucial. It is essential to compare sophisticated models to basic ones like a lag one-day model or climatological model to ensure significant improvements. Any complex model must significantly outperform basic models to justify its complexity.
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