The field of machine learning has matured to the point where many
sophisticated learning approaches can be applied to practical
applications. Thus it is of critical importance that researchers
have the proper tools to evaluate learning approaches and
understand the underlying issues. This book examines various
aspects of the evaluation process with an emphasis on
classification algorithms. The authors describe several techniques
for classifier performance assessment, error estimation and
resampling, obtaining statistical significance as well as selecting
appropriate domains for evaluation. They also present a unified
evaluation framework and highlight how different components of
evaluation are both significantly interrelated and interdependent.
The techniques presented in the book are illustrated using R and
WEKA, facilitating better practical insight as well as
implementation. Aimed at researchers in the theory and applications
of machine learning, this book offers a solid basis for conducting
performance evaluations of algorithms in practical settings.
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