## 6 Evaluation Metrics
### Portfolio Evaluation Metrics  
Measures of the overall performance of the portfolio are an essential component of the decision to invest. Tracking and measuring return and risk are the two basic metrics for evaluating portfolio performance. Several other indicators may be used from within those  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Model** & **Description** & **Formula** & **Article** \\ \hline
**Mean Absolute Error** &
\begin{tabular}{c} The average absolute \\ difference between the \\ values that are fitted by the \\ model and the historical \\ data that have been \\ observed. \\ \end{tabular} & MAE = \(\frac{1}{N}\sum\limits_{i=1}^{N}\left|Y_{i}-\hat{Y}_{i}\right|\) & [10; 27; 29; 57] \\ \hline
**Mean Squared Error** &
\begin{tabular}{c} The sum of squared \\ differences between \\ model-fitted values and \\ observed values divided by \\ points minus the model’s \\ parameters. \\ \end{tabular} & MSE = \(\frac{1}{N}\sum\limits_{i=1}^{N}\left(Y_{i}-\hat{Y}_{i}\right)^{2}\) & [10; 27; 57; 79] \\ \hline
**Root Mean Squared** &
\begin{tabular}{c} The root square of the mean \\ square error. It uses the \\ same scale as the values \\ that were seen in the data. \\ \end{tabular} & RMSE = \(\sqrt{\sum\limits_{i=1}^{N}\left(\frac{Y_{i}-\hat{Y}_{i}}{N}\right)^{2}}\) & [27; 28; 29; 80] \\ \hline
**Mean Absolute** &
\begin{tabular}{c} The average absolute \\ between the values \\ predicted by the model and \\ the values actually found in \\ the data. \\ \end{tabular} & MAPE = \(\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{Y_{i}-\hat{Y}_{i}}{Y_{i}}\right|\) & [10; 27; 57] \\ \hline
**Classification Accuracy** &
\begin{tabular}{c} It determines the frequency \\ with which the model \\ correctly predicts the \\ output. \\ \end{tabular} & \(\frac{\text{Accuracy }}{\text{TP + TN}}\) & [30; 37; 49; 57; 66; 68] \\ \hline
**Misclassification rate** &
\begin{tabular}{c} It determines the frequency \\ with which the model \\ correctly predicts the \\ output. \\ \end{tabular} & \(\frac{\text{Error rate }}{\text{TP + TN}}\) & \\ \hline
**Precision** &
\begin{tabular}{c} Precision is also known as \\ Positive Predictive Value. \\ Precision is the proportion \\ of correct positive \\ predictions to total positive \\ predictions. \\ \end{tabular} & Precision = \(\frac{\text{TP}}{\text{TP + TP}}\) & [30; 37; 49; 57; 66; 68] \\ \hline
**Recall** &
\begin{tabular}{c} It is defined as the positive \\ classes that our model \\ accurately predicted out of \\ a total of all positive \\ classes. \\ \end{tabular} & Recall = \(\frac{\text{TP}}{\text{TP + TN}}\) & [30; 37; 49; 57; 66; 68] \\ \hline
**F1-Score** &
\begin{tabular}{c} It is difficult to compare \\ two models if one has low \\ precision and the other has \\ a high recall. We can use \\ F-score for this. If the recall \\ equals the precision, the \\ F-score is maximum \\ \end{tabular} & \(2\times\frac{\text{F1 Score }}{\text{Precision + Recall}}\) & [30; 37; 49; 57; 66; 68] \\ \hline
**AUC** & \
\begin{tabular}{c} AUC is the probability that \\ a random positive case \\ comes after a random \\ negative example. AUC \\ varies from 0 to 1. A model \\ whose forecasts are 100\% \\ inaccurate has an AUC of \\ 0.0; one whose predictions \\ are 100\% right has an AUC \\ of 1.0. \\ \end{tabular} &
\begin{tabular}{c} AUC = \\ Recall + Sensitivity \\ 2 \\ \end{tabular} & [37] \\ \hline \hline \end{tabular}
\end{table}
Table 13: Model evaluation metrics and article used.  
broad categories to track the development of an investment portfolio over time, below Table 14 shows seven performance and risk criteria used by investors to assess portfolios.