Search Results

9 results for “loss”

Formulae (9)

MSE = \frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2

Average of squared differences between predicted and actual values.

RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}

Square root of MSE, in the same units as the target variable.

MAE = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|

Average of absolute differences between predictions and actual values.

L = -\frac{1}{n}\sum[y\log(\hat{y}) + (1-y)\log(1-\hat{y})]

Loss function for binary classification.

L = -\sum_{i=1}^{K} y_i \log(\hat{y}_i)

Loss for multi-class classification with one-hot labels.

\Delta m = \frac{\text{deficit}}{7700}

Body-fat lost from a total calorie deficit.

\Delta m = \frac{E_{deficit}}{7700}

Approximate body-fat change (kg) from a cumulative calorie deficit or surplus.

ROI = \frac{V_f - V_i}{V_i} \times 100\%

Percentage gain or loss on an investment.

\Delta w = \frac{\text{deficit} \times days}{7700}

Approximate body-mass loss from a calorie deficit (kg).