Errors and Loss

Errors - differences between predicted values and actual values, also called residuals - are a key part of statistical models.  They form the raw material for various metrics of predictive model performance (accuracy, precision, recall, lift, etc.), and also the basis for diagnostics on descriptive…

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Unforeseen Consequences in Data Science

Unforeseen Consequences in Data Science After the massive Exxon Valdez oil spill, states passed laws boosting the liability of tanker companies for future spills.  The result was not as intended: fly-by-night companies, whose bankruptcy would not be consequential, took over the trade. In this blog…

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