Unverified50% confidenceFactExact time
Out-of-sample R² is defined as 1 - Σ(y_i - ŷ_i)² / Σ(y_i - ȳ)², and when the model's predictions are worse than simply using the mean, this value drops below 0.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
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Related Claims
UnverifiedThe R² coefficient of determination ranges between 0 and 1, calculated by the formula R² = 1 - RSS/TSS, and represents the proportion of the total variance in the dependent variable explained by the model.69% similarUnverifiedFocal Loss adds a modulating factor (1-p_t)^γ before the standard cross-entropy loss, with γ typically set to 2, causing the model to focus on hard examples.69% similarUnverifiedRidge regression shrinks regression coefficients toward zero but does not set them to zero by adding an L2 regularization term to the loss function, whereas LASSO (L1 regularization) produces sparse solutions.68% similarUnverifiedThe sample variance estimator S_n is defined as (1/n)∑(X_i - X̄)²67% similarUnverifiedThe endpoint metric used in the evaluation is the difference of 'model minus uniform control group', where a positive value represents performing worse than the control.66% similar
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