\section{Iris}

We executed a KCross validation with the IRIS dataset\footnote{http://archive.ics.uci.edu/ml/datasets/Iris}, testing the following algorithms:
\begin{itemize}
  \item[\textbullet] Random Classifier
  \item[\textbullet] Majority Classifier
  \item[\textbullet] 3NN
  \item[\textbullet] 5NN
\end{itemize}

\begin{figure}[!h]
	\begin{center}
		\includegraphics[width=12cm]{images/iris}
	\end{center}
	\caption{Benchmark results on IRIS dataset}
\end{figure}

In the case of the IRIS dataset, KNN algorithm is very efficient (over 95\%).\\
Random and Majority algorithms results are lower than tests with the donut. Indeed, the IRIS dataset has 3 labels, and the donut 2. Therefore, the probability of good prediction of the Random Classifier is 33\%. The majority classifier is not efficient either as the distributions of each different labels are the same.