### ROC Chart

#### Visualization of the performance of a binary classifier system as its discrimination threshold is varied. A presentation illustrating the trade off between the true positive rate and the true negative rate of a classifier. Each point represents the classifier performance for a given threshold or ranking cut-off point.

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### ROC Hull Chart

#### Visualization of the optimal performance of a binary classifier system as its discrimination threshold is varied. The ROC Hull is the upper convex hull of the ROC chart. Each point represents the classifier performance for a given threshold or ranking cut-off point. Points on the ROC Hull represent an optimal performance of the classifier for certain misclassification costs.

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### PR Chart

#### Visualization of the performance of a binary classifier system as its discrimination threshold is varied. The PR (precision recall) curve presents the trade off between the precision (the fraction of examples classified as positive that are truly positive) and the recall or true positive rate. Each point represents the classifier performance for a given threshold or ranking cut-off point.

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### Lift Chart

#### Visualization of the performance of a binary classifier system as its discrimination threshold is varied. The Lift curve plots the true positive rate (also found in ROC and PR curves) against the predicted positive rate (the fraction of examples, classified as positive). Each point represents the classifier performance for a given threshold or ranking cut-off point.

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### Cost Chart

#### Visualization of the expected cost of a binary classifier system as the operating conditions vary The Cost curve plots the normalized expected cost of the classifier as a function of the skew (fraction of positive examples multiplied by the cost of misclassifying a positive example) of the data on which it is deployed. Lines and points on the cost curve correspond to points and lines on the ROC curve of the classifier.

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References:

Drummond and Holte (2006) - Cost curves: An improved method for visualizing classifier performance

### Kendall Chart

#### Visualization of the expected cost of a binary classifier system as its discrimination threshold is varied. The Kendall chart is defined as the difference between the normalized expected cost of the classifier and the normalized expected cost of an ideal classifier. Costs for both classifiers are calculated using the rate-driven threshold choice method.

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References:

Hernández-Orallo, Flach and Ferri (2013) - ROC curves in cost space

### Rate-Driven Chart

#### Visualization of the expected cost of a binary classifier system as its discrimination threshold is varied. 