This architecture addresses both ethical concerns regarding autonomous lethal weapons and tactical concerns regarding reliability. In the heat of battle, human judgment remains the most reliable safeguard against friendly fire or civilian casualties.
| Term | Meaning | Purpose | |------|---------|---------| | | Speed for best rate of climb | Maximizes ROC-M | | Vx | Speed for best angle of climb | Steepest climb path (clearing an obstacle) | | ROC-M | Maximum possible vertical speed | Achieved only at Vy under given conditions | mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] =
y_bin = label_binarize(y, classes=[0, 1, 2]) n_classes = y_bin.shape[1] mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] =
: It ensures operators understand Digital Selective Calling (DSC) and emergency procedures [26]. mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] =
mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
ROC-M solves this by breaking down the multi-class problem into several binary comparisons. The most common approaches are:
Instead of giving each class equal weight (Macro), you weight each class by its support (number of true instances). This is a middle ground between Macro and Micro.