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\frametitle{Method 3: Nearest Neighbor}
\Large \textbf{Nearest Neighbor} \begin{itemize} \item Nearest Neighbor uses the notion of similarity to assign class labels; it is based on the smoothness assumption that points which are nearby in input space should have similar outputs. \item It does this by specifying a similarity (or distance) metric, and at prediction time it simply searches for the k most similar among the training examples to a given test example. \end{itemize}
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\frametitle{Method 3: Nearest Neighbor}
\Large
\textbf{Nearest Neighbor}
\begin{itemize}
\item The prediction is then either a majority vote of those k training examples or a vote weighted by similarity. \item The parameter k specifies the smoothness of the decision surface.\item The decision surface of a k-nearest neighbor classifier can be illustrated by the \textbf{Voronoi tesselation} of the training data, that show you the regions of constant respones.
\end{itemize}
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\frametitle{Method 3: Nearest Neighbor}
\begin{figure}
\centering
\includegraphics[width=1.1\linewidth]{sklcass/sklclass16}
\end{figure}
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\frametitle{Method 3: Nearest Neighbor}
\Large
\textbf{Nearest Neighbours}
\begin{itemize}
\item Yet Nearest Neighbor differs fundamentally from the above models in that it is a so-called non-parametric technique: the number of parameters of the model can grow infinitely as the size of the training data grows. \item Furthermore, it can model non-linear decision boundaries, something that is important for the first two datasets: moons and circles. \end{itemize}
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\frametitle{Method 3: Nearest Neighbor}
\large
\begin{framed}
\begin{verbatim}
from sklearn.neighbors import KNeighborsClassifier
est = KNeighborsClassifier(n_neighbors=1)
plot_datasets(est)
\end{verbatim}
\end{framed}
\begin{figure}
\centering
\includegraphics[width=1.1\linewidth]{sklcass/sklclass16}
\begin{itemize}
\item If we increase k we enforce the smoothness assumption. \item This can be seen by comparing the decision boundaries in the plots below where k=5 to those above where k=1. \end{itemize} { \large \begin{framed} \begin{verbatim} est = KNeighborsClassifier(n_neighbors=5) plot_datasets(est)
\end{verbatim}
\begin{figure} \centering \includegraphics[width=1.1\linewidth]{sklcass/sklclass20}
\end{figure}