From f93594007dffd12738446ef29c3c0af99c3e8353 Mon Sep 17 00:00:00 2001 From: "Jip J. Dekker" Date: Fri, 25 May 2018 14:55:13 +1000 Subject: [PATCH] A little more cleanup --- mini_proj/report/waldo.tex | 31 ++++++++++++++----------------- 1 file changed, 14 insertions(+), 17 deletions(-) diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex index 28d2689..4c1881c 100644 --- a/mini_proj/report/waldo.tex +++ b/mini_proj/report/waldo.tex @@ -98,7 +98,7 @@ 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}