Consider the following data set comprised of three binary input
attributes ($A_1, A_2$, and $A_3$) and one binary output:
$$ \begin{array} {|r|r|}\hline \textbf{Example} & A_1 & A_2 & A_3 & Output\space y \\ \hline \textbf{x}_1 & 1 & 0 & 0 & 0 \\ \textbf{x}_2 & 1 & 0 & 1 & 0 \\ \textbf{x}_3 & 0 & 1 & 0 & 0 \\ \textbf{x}_4 & 1 & 1 & 1 & 1 \\ \textbf{x}_5 & 1 & 1 & 0 & 1 \\ \hline \end{array} $$ Use the algorithm in Figure DTL-algorithm (page DTL-algorithm) to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.
$$ \begin{array} {|r|r|}\hline \textbf{Example} & A_1 & A_2 & A_3 & Output\space y \\ \hline \textbf{x}_1 & 1 & 0 & 0 & 0 \\ \textbf{x}_2 & 1 & 0 & 1 & 0 \\ \textbf{x}_3 & 0 & 1 & 0 & 0 \\ \textbf{x}_4 & 1 & 1 & 1 & 1 \\ \textbf{x}_5 & 1 & 1 & 0 & 1 \\ \hline \end{array} $$ Use the algorithm in Figure DTL-algorithm (page DTL-algorithm) to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.
Consider the following data set comprised of three binary input
attributes ($A_1, A_2$, and $A_3$) and one binary output:
$$
\begin{array}
{|r|r|}\hline \textbf{Example} & A_1 & A_2 & A_3 & Output\space y \\
\hline \textbf{x}_1 & 1 & 0 & 0 & 0 \\
\textbf{x}_2 & 1 & 0 & 1 & 0 \\
\textbf{x}_3 & 0 & 1 & 0 & 0 \\
\textbf{x}_4 & 1 & 1 & 1 & 1 \\
\textbf{x}_5 & 1 & 1 & 0 & 1 \\
\hline
\end{array}
$$
Use the algorithm in Figure DTL-algorithm
(page DTL-algorithm) to learn a decision tree for these data. Show the
computations made to determine the attribute to split at each node.