ROC Plot: Clearly explained 🔥

💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).

🤔 Specificity and Sensitivity

The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.

Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.

🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.

😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).

A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.

📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.

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