001 Classification-Performance-Standard.ipynb
Classification performance standards such as accuracy, precision, recall, confusion matrix, F1, ROC, AUC. Take MNIST as example.
002 Classification-Performance-Standard.ipynb
The framework to evaluate multi-classification classifier automatically.
Confusion matrix
Predict Positive | Predict Negative | |
---|---|---|
Actual Positive | TP(True Positive) | FN(False Negative) |
Actual Negative | FP(False Positive) | TN(True Negative) |
The vertical axis of the ROC(Receiver Operating Characteristic) curve is TPR(True Positive Rate) and the horizontal axis is FPR(False Positive Rate).
AUC is the area under ROC curve.