From: A comprehensive evaluation of ensemble learning for stock-market prediction
Acronym | Full name | Formula |
---|---|---|
RMSE | Root mean squared error | \(RMSE = \sqrt {\frac{1}{n}} \mathop \sum \limits_{i = 1}^{n} \left( {t_{i} - y_{i} } \right)\) |
MAE | Mean absolute error | \(MAE = \frac{1}{n}\mathop \sum \limits_{i = 1}^{n} \left( {t_{i} - y_{i} } \right)\) |
R2 | The F1-score | \(R^{2} = \frac{{2 \times P^{R} \times R^{R} }}{{P^{R} + R^{R} }}\) |
RMSLE | Root mean squared logarithmic error | \(RMSLE = \surd \left( {MSE \left( {{ \log }(y_{n} + 1} \right), { \log }(\hat{y}_{n} + 1} \right))\) |
ACC | Accuracy | \(Acc = \frac{TN + TP}{FP + TP + TN + FN}\) |
REC | Recall | \(REC = \frac{TP}{TP + FN}\) |
PRE | Precision | \(PRE = \frac{TP}{TP + FP}\) |
AUC | Area under ROC curve | \(AUC = \mathop \smallint \limits_{0}^{1} \frac{TP}{{\left( {TP + FN} \right)}}d\frac{FP}{(FP + TN} = \mathop \smallint \limits_{0}^{1} \frac{TP}{P}d\frac{FP}{N}\) |
MedAE | Median absolute error | \(MedAE\left( {y,\hat{y}} \right) = median\left( {\left| {y_{1} - \hat{y}_{1} } \right|, \ldots ,\left| {y_{n} - \hat{y}_{n} } \right|} \right)\) |
EVS | Explained variance score | Â |
Mean | Mean | Â |
STD | Standard deviation | Â |