Evaluating Learning Algorithms: A Classification Perspective by Nathalie Japkowicz

By Nathalie Japkowicz

The sector of computing device studying has matured to the purpose the place many subtle studying techniques may be utilized to useful functions. hence it truly is of severe significance that researchers have the right kind instruments to judge studying methods and comprehend the underlying matters. This e-book examines quite a few points of the overview technique with an emphasis on category algorithms. The authors describe a number of thoughts for classifier functionality evaluation, mistakes estimation and resampling, acquiring statistical value in addition to opting for applicable domain names for evaluate. additionally they current a unified overview framework and spotlight how assorted parts of evaluate are either considerably interrelated and interdependent. The innovations awarded within the booklet are illustrated utilizing R and WEKA facilitating larger useful perception in addition to implementation. geared toward researchers within the thought and functions of computing device studying, this booklet deals a superior foundation for accomplishing functionality reviews of algorithms in sensible settings.

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The size of the training set has an effect on the loss function behavior too. Consequently, what we are interested in is averaging the loss function estimate over several training sets of the same size. However, such an averaging is easier said than done. In practice, we generally do not have access to large amounts of data that can enable us to have several training sets. A solution to this 36 Machine Learning and Statistics Overview problem can come in the form of data resampling. We explore some of the prominent techniques of data resampling in Chapter 5 and also study the related issues.

The space of classifiers or functions F is referred to as the hypothesis space or classifier space. However, we reserve the term hypothesis for its more conventional usage in statistical significance testing and use the term classifier space from here onward when referring to the space of functions explored by a learning algorithm. Each training example x is basically an instantiation of a random vector in X , and hence we also refer to these as instances. Different configurations of the preceding setting yield different learning problems.

As suggested by Witten and Frank, 2005b), the independence assumption between the test sets is then violated in addition to the one concerning the classifiers. 4 Broader Issues with Evaluation Approaches 15 should, at the least, be aware of all the assumptions that are violated and the possible consequences of this action. Alternatives to cross-validation testing in such scenarios in which statistical significance testing effects are kept in mind have been suggested. Two of the main resampling statistics that have appeared to be useful, but have so far eluded the community, are bootstrapping and randomization.

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