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Jip J. Dekker 2018-05-25 14:55:13 +10:00
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mini_proj/report/waldo.tex
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their basic versions. In contrast, we will use different neural network
architectures, as this method is currently the most used for image
classification.
\subsection{Classical Machine Learning Methods}
The following paragraphs will give only brief descriptions of the different
@ -286,22 +286,19 @@
\end{itemize}
\emph{Accuracy} is a common performance metric used in Machine Learning,
however in classification problems where the training data is heavily
biased toward one category, sometimes a model will learn to optimize its
accuracy by classifying all instances as one category. I.e. the
classifier will classify all images that do not contain Waldo as not
containing Waldo, but will also classify all images containing Waldo as
not containing Waldo. Thus we use, other metrics to measure performance
as well.
\\
\par
\emph{Precision} returns the percentage of classifications of Waldo that
are actually Waldo. \emph{Recall} returns the percentage of Waldos that
were actually predicted as Waldo. In the case of a classifier that
classifies all things as Waldo, the recall would be 0. \emph{F1-Measure}
returns a combination of precision and recall that heavily penalizes
classifiers that perform poorly in either precision or recall.
% Kelvin End
however in classification problems where the training data is heavily biased
toward one category, sometimes a model will learn to optimize its accuracy
by classifying all instances as one category. I.e. the classifier will
classify all images that do not contain Waldo as not containing Waldo, but
will also classify all images containing Waldo as not containing Waldo. Thus
we use, other metrics to measure performance as well. \\
\emph{Precision} returns the percentage of classifications of Waldo that are
actually Waldo. \emph{Recall} returns the percentage of Waldos that were
actually predicted as Waldo. In the case of a classifier that classifies all
things as Waldo, the recall would be 0. \emph{F1-Measure} returns a
combination of precision and recall that heavily penalizes classifiers that
perform poorly in either precision or recall.
\section{Results} \label{sec:results}