analysis and visualization of classifier performance comparison under imprecise class and cost...

F. Provost and T. Fawcett

Confusion Matrix

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Introduction Data mining requires:

Experiments with a wide variety of learning algorithms

Using different algorithm parametersVarying output threshold valuesUsing different training regimens

Using accuracy alone is inadequate because:Class distributions are skewedMisclassification (FP, FN) costs are not uniform

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Class Distributions -Problems with Acc. … assumes that class distribution among

examples is constant and relatively balanced (-which is not the case in real life-)

Classifiers are generally used to scan ‘large number of normal entities’ to find ‘small number of unusual ones’Looking for defrauded customersChecking an assembly lineSkews of 106 were reported (Clearwater & Stern 1991)

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Misclassification Costs -Problems with Acc. ‘Equal error costs’ does not hold in real

life problemsDisease tests, fraud detection…

Instead of maximizing the accuracy, we need to minimize the error cost.

Cost = FP • c(Y,n) + FN • c(N,p)

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ROC Plot and ROC Area Receiver Operator Characteristic Developed in WWII to statistically model

“false positive” and “false negative” detections of radar operators

Becoming more popular in ML and standard measure in medicine and biology

However does poor job on deciding the choice of classifiers

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ROC graph of four classifiers

Informally a point in ROC space is better than the other if it is to the northwest.

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Iso-performance Lines Expected cost of a classification by a classifier (FP,TP):

Therefore, two points havethe same performance if

Iso-perf. line: All classifierscorresponding to points on the line have the same expected cost.

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ROC Convex Hull If a point is not on the

convex hull the classifier represented by that point cannot be optimal.

In this example B and D cannot be optimal because none or their points are on the convex hull.

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How to use the ROC Convex Hull

p(n):p(p) = 10:1 Scenario A:

c(N,p) = c(Y,n) m(iso_perf) = 10

Scenario B: c(N,p) = 100 • c(Y,n) m(iso_perf) = 0.1

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Adding New Classifiers Adding new classifiers

may or may not extend the existing hull.

E may be optimal under some circumstances since it extends the hull

F and G cannot be optimal

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What if distributions & costs are unknown? ROC convex hull gives

us an idea about all classifiers that may be optimal under any conditions.

With complete information the method identifies the optimal classifiers.

In between ?

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Sensitivity Analysis Imprecise distribution info

defines a range of slopes for iso-perf lines.

p(n):p(p) = 10:1 Scenario C:

○ $5 < c(Y,n) < $10○ $500 < c(N,p) < $1000○ 0.05 < m(iso_perf) < 0.2

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Sensitivity Analysis - 2 Imprecise distribution info

defines a range of slopes for iso-perf lines.

p(n):p(p) = 10:1 Scenario D:

○ 0.2 < m(iso_perf) < 2

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Sensitivity Analysis - 3 Can “do nothing” strategy

be better than any of the available classifiers?

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Conclusion Accuracy alone as a performance metric is

incapable for various reasons ROC plots give more accurate information about

the performance of classifiers ROC convex hull method

Is an efficient solution to the problem of comparing multiple classifiers in imprecise environments

Allows us to incorporate new classifiers easilyAllows us to select the classifiers that are potentially

optimal

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