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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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Precision-Recall plot - Clearly explained
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
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