10/26/2022 0 Comments Rapidminer studio 6.4 downloadAUPRC performance and the number of features involved in model building (in brackets) are showed for each model (rows) and feature selection technique (columns). In bold, the best AUPRC performances for each row are illustrated, revealing the best feature selection method for each model. AUPRC (Area Under the Precision Recall Curve) performance and the number of features involved in model building (in brackets) are showed for each model (rows) and feature selection technique (columns). All experimental results are summarized in Table 3. AUPRC is a less forgiving measure, and a high value indicates that a classification model makes very few mistakes. By varying the threshold, the precision can be calculated at the threshold that achieves that recall ratio. recall ratio (this is TPR, sometimes referred to as sensitivity)(TP: True Positive, FP: False Positive, TPR: True Positive Ratio). AUPRC measured the fraction of negatives misclassified as positives and resulting in a plot representing precision (TP/(TP+FP)) vs. Other parameters, frequently published in this respect, such as AUC (Area Under the ROC Curve) and Accuracy are independent of class size ratios, but often provide mislead- ing results in unbalanced data scenarios. Because of the unbalanced nature of data we calculated AUPRC (Area Under the Precision Recall Curve) values as evaluation parameter for model comparison. optimization (parameters or feature sets), sensitive for over-fitting and reduced generalization of predictive models, are cross-validated on training sets (70% of initial data).
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