Confusion Matrix

I learned about this a while ago.

• https://www.v7labs.com/blog/mean-average-precision
• https://blog.paperspace.com/deep-learning-metrics-precision-recall-accuracy/

To create a confusion matrix, we need four attributes:

• True Positives (TP): The model predicted a label and matches correctly as per ground truth.
• True Negatives (TN): The model does not predict the label and is not a part of the ground truth.
• False Positives (FP): The model predicted a label, but it is not a part of the ground truth (Type I Error).
• False Negatives (FN): The model does not predict a label, but it is part of the ground truth. (Type II Error).

$\text{Sensitivity/Recall}=\frac{TP}{TP+FN}$ $\text{Specificity}=\frac{TN}{TN+FP}$ $\text{Precision}=\frac{TP}{TP+FP}$ $\text{Negative Predictive Value}=\frac{TN}{TN+FN}$ $\text{Accuracy}=\frac{TP+FP}{TP+FP+TN+FN}$

However, you cannot solely rely on Accuracy (ML) to define that your model is good, see page for more details.

Curves:

• Precision-Recall Curve
• ROC Curve https://www.dataschool.io/roc-curves-and-auc-explained/

More: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/