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Coronavirus: To Test or Not to Test

In recent years, under the influence of statisticians, the medical profession has dialed back on screening tests.  With relatively rare conditions, widespread testing yields many false positives and doctor visits, whose collective cost can outweigh benefits.  Coronavirus advice follows this line – testing is limited to the truly ill (this is also due to aContinue reading “Coronavirus: To Test or Not to Test”

Regularized Model

In building statistical and machine learning models, regularization is the addition of penalty terms to predictor coefficients to discourage complex models that would otherwise overfit the data.  An example is ridge regression.