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Performance Curves

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Introduction What are ROC Curves? Evaluating Classifier Performance Using perfcurve References

Introduction

After a classification algorithm such as NaiveBayes or TreeBagger has trained on data, you may want to examine the performance of this algorithm on a specific test dataset. One common way of doing this would be to compute a gross measure of performance such as quadratic loss, accuracy, such as quadratic loss or accuracy, averaged over the entire test dataset.

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What are ROC Curves?

You may want to inspect the classifier performance more closely, for example, by plotting a Receiver Operating Characteristic (ROC) curve. By definition, a ROC curve [1,2] shows true positive rate versus false positive rate (equivalently, sensitivity versus 1–specificity) for different thresholds of the classifier output. You can use it, for example, to find the threshold that maximizes the classification accuracy or to assess, in more broad terms, how the classifier performs in the regions of high sensitivity and high specificity.

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Evaluating Classifier Performance Using perfcurve

perfcurve computes measures for a plot of classifier performance. You can use this utility to evaluate classifier performance on test data after you train the classifier. Various measures such as mean squared error, classification error, or exponential loss can summarize the predictive power of a classifier in a single number. However, a performance curve offers more information as it lets you explore the classifier performance across a range of thresholds on its output.

You can use perfcurve with any classifier or, more broadly, with any method that returns a numeric score for an instance of input data. By convention adopted here,

• A high score returned by a classifier for any given instance signifies that the instance is likely from the positive class.

• A low score signifies that the instance is likely from the negative classes.

For some classifiers, you can interpret the score as the posterior probability of observing an instance of the positive class at point X. An example of such a score is the fraction of positive observations in a leaf of a decision tree. In this case, scores fall into the range from 0 to 1 and scores from positive and negative classes add up to unity. Other methods can return scores ranging between minus and plus infinity, without any obvious mapping from the score to the posterior class probability.

perfcurve does not impose any requirements on the input score range. Because of this lack of normalization, you can use perfcurve to process scores returned by any classification, regression, or fit method. perfcurve does not make any assumptions about the nature of input scores or relationships between the scores for different classes. As an example, consider a problem with three classes, A, B, and C, and assume that the scores returned by some classifier for two instances are as follows:

	A	B	C
instance 1	0.4	0.5	0.1
instance 2	0.4	0.1	0.5

If you want to compute a performance curve for separation of classes A and B, with C ignored, you need to address the ambiguity in selecting A over B. You could opt to use the score ratio, s(A)/s(B), or score difference, s(A)-s(B); this choice could depend on the nature of these scores and their normalization. perfcurve always takes one score per instance. If you only supply scores for class A, perfcurve does not distinguish between observations 1 and 2. The performance curve in this case may not be optimal.

perfcurve is intended for use with classifiers that return scores, not those that return only predicted classes. As a counter-example, consider a decision tree that returns only hard classification labels, 0 or 1, for data with two classes. In this case, the performance curve reduces to a single point because classified instances can be split into positive and negative categories in one way only.

For input, perfcurve takes true class labels for some data and scores assigned by a classifier to these data. By default, this utility computes a Receiver Operating Characteristic (ROC) curve and returns values of 1–specificity, or false positive rate, for X and sensitivity, or true positive rate, for Y. You can choose other criteria for X and Y by selecting one out of several provided criteria or specifying an arbitrary criterion through an anonymous function. You can display the computed performance curve using plot(X,Y).

perfcurve can compute values for various criteria to plot either on the x- or the y-axis. All such criteria are described by a 2-by-2 confusion matrix, a 2-by-2 cost matrix, and a 2-by-1 vector of scales applied to class counts.

The confusion matrix, C, is defined as

,

where

• P stands for "positive".

• N stands for "negative".

• T stands for "true".

• F stands for "false".

For example, the first row of the confusion matrix defines how the classifier identifies instances of the positive class: C(1,1) is the count of correctly identified positive instances and C(1,2) is the count of positive instances misidentified as negative.

The cost matrix defines the cost of misclassification for each category:

,

where Cost(I|J) is the cost of assigning an instance of class J to class I. Usually Cost(I|J)=0 for I=J. For flexibility, perfcurve allows you to specify nonzero costs for correct classification as well.

The two scales include prior information about class probabilities. perfcurve computes these scales by taking scale(P)=prior(P)*N and scale(N)=prioer(N)*P and normalizing the sum scale(P)+scale(N) to 1. P=TP+FN and N=TN+FP are the total instance counts in the positive and negative class, respectively. The function then applies the scales as multiplicative factors to the counts from the corresponding class: perfcurve multiplies counts from the positive class by scale(P) and counts from the negative class by scale(N). Consider, for example, computation of positive predictive value, PPV = TP/(TP+FP). TP counts come from the positive class and FP counts come from the negative class. Therefore, you need to scale TP by scale(P) and FP by scale(N), and the modified formula for PPV with prior probabilities taken into account is now:

If all scores in the data are above a certain threshold, perfcurve classifies all instances as 'positive'. This means that TP is the total number of instances in the positive class and FP is the total number of instances in the negative class. In this case, PPV is simply given by the prior:

The perfcurve function returns two vectors, X and Y, of performance measures. Each measure is some function of confusion, cost, and scale values. You can request specific measures by name or provide a function handle to compute a custom measure. The function you provide should take confusion, cost, and scale as its three inputs and return a vector of output values.

The criterion for X must be a monotone function of the positive classification count, or equivalently, threshold for the supplied scores. If perfcurve cannot perform a one-to-one mapping between values of the X criterion and score thresholds, it exits with an error message.

By default, perfcurve computes values of the X and Y criteria for all possible score thresholds. Alternatively, it can compute a reduced number of specific X values supplied as an input argument. In either case, for M requested values, perfcurve computes M+1 values for X and Y. The first value out of these M+1 values is special. perfcurve computes it by setting the TP instance count to zero and setting TN to the total count in the negative class. This value corresponds to the 'reject all' threshold. On a standard ROC curve, this translates into an extra point placed at (0,0).

If there are NaN values among input scores, perfcurve can process them in either of two ways:

• It can discard rows with NaN scores.

• It can add them to false classification counts in the respective class.

That is, for any threshold, instances with NaN scores from the positive class are counted as false negative (FN), and instances with NaN scores from the negative class are counted as false positive (FP). In this case, the first value of X or Y is computed by setting TP to zero and setting TN to the total count minus the NaN count in the negative class. For illustration, consider an example with two rows in the positive and two rows in the negative class, each pair having a NaN score:

Class	Score
Negative	0.2
Negative	NaN
Positive	0.7
Positive	NaN

If you discard rows with NaN scores, then as the score cutoff varies, perfcurve computes performance measures as in the following table. For example, a cutoff of 0.5 corresponds to the middle row where rows 1 and 3 are classified correctly, and rows 2 and 4 are omitted.

TP	FN	FP	TN
0	1	0	1
1	0	0	1
1	0	1	0

If you add rows with NaN scores to the false category in their respective classes, perfcurve computes performance measures as in the following table. For example, a cutoff of 0.5 corresponds to the middle row where now rows 2 and 4 are counted as incorrectly classified. Notice that only the FN and FP columns differ between these two tables.

TP	FN	FP	TN
0	2	1	1
1	1	1	1
1	1	2	0

By default perfcurve does not return the 'reject all' values for X and Y. It can optionally do so upon request.

For data with three or more classes, perfcurve takes one positive class and a list of negative classes for input. The function computes the X and Y values using counts in the positive class to estimate TP and FN, and using counts in all negative classes to estimate TN and FP. perfcurve can optionally compute Y values for each negative class separately and, in addition to Y, return a matrix of size M-by-C, where M is the number of elements in X or Y and C is the number of negative classes. You can use this functionality to monitor components of the negative class contribution. For example, you can plot TP counts on the X-axis and FP counts on the Y-axis. In this case, the returned matrix shows how the FP component is split across negative classes.

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References

[1] T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers, 2004.

[2] M. Zweig and G. Campbell, Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine, Clin. Chem. 39/4, 561-577, 1993.

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Regression and Classification by Bagging Decision Trees

Markov Models

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