From: Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion
Category | Parameters | Definition | Meaning |
---|---|---|---|
Classification prediction models | True positive (TP) | Real label = 1 and predicted label = 1 | Number of correctly classified positive results |
True negative (TN) | Real label = 0 and predicted label = 0 | Number of correctly classified negative results | |
False positive (FP) | Real label = 0 and predicted label = 1 | Number of misclassified positive results | |
False negative (FN) | Real label = 1 and predicted label = 0 | Number of misclassified negative results | |
Accuracy (ACC) | ACC = (TP + TN)/(TP + TN + FP + FN) | Overall prediction accuracy | |
Sensitivity (SE) | SE = TP/(TP + FN) | Prediction accuracy of the positive set | |
Specificity (SP) | SP = TN/(TN + FP) | Prediction accuracy of the negative set | |
Precision | Precision = TP/(TP + FP) | Efficiency of positive results prediction | |
Recall | Recall = TP/(TP + FN) | Coverage of positive results prediction | |
Index F (F1) | F1 = 2Precison * Recall/(Precision + Recall) | Evaluation of the comprehensive performance of the models | |
Receiver operating characteristic (ROC) curve area under the Roc curve (AUC) | The probability that a randomly chosen positive example is ranked higher than a randomly chosen negative example | The performance of the classification model as its discrimination threshold is varied | |
Regression prediction model | Squared correlation coefficient (Q2/R2) | Q2/R2 = \(1 - \frac{{\mathop \sum \nolimits_{i = 1}^{m} \left( {y_{i} - \widehat{{y_{i} }}} \right)^{2} }}{{\mathop \sum \nolimits_{i = 1}^{m} \left( {y_{i} - \overline{y}_{i} } \right)^{2} }}\) | Squared correlation coefficient |
Mean absolute error of cross validation (MAE) | MAE = \(\frac{1}{m}\mathop \sum \limits_{i = 1}^{m} \left( {y_{i} - \widehat{{y_{i} }}} \right)^{2}\) | Mean absolute error of cross validation | |
Root mean squared error (RMSE) | RMSE = \(\surd \left( {{\text{MAE}}} \right)\) | Root mean squared error |