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This topic has appeared in the English Wikipedia rankings 1 time. It first appeared on 2026-05-18 and was most recently seen on 2026-05-18.
In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis while keeping the risk of overfitting and finding spurious correlations low. The rule states that one predictive variable can be studied for every ten events. For logistic regression the number of events is given by the size of the smallest of the outcome categories, and for survival analysis it is given by the number of uncensored events. In other words: for each feature we need 10 observations/labels.
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