Consider the following data set comprised of three binary input
attributes ($A_1, A_2$, and $A_3$) and one binary output:
| $\quad \textbf{Example}$ | $\quad A_1\quad$ | $\quad A_2\quad$ | $\quad A_3\quad$ | $\quad Output\space y$ | | --- | --- | --- | --- | --- | | $\textbf{x}_1$ | 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 | 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.
| $\quad \textbf{Example}$ | $\quad A_1\quad$ | $\quad A_2\quad$ | $\quad A_3\quad$ | $\quad Output\space y$ | | --- | --- | --- | --- | --- | | $\textbf{x}_1$ | 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 | 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:
| $\quad \textbf{Example}$ | $\quad A_1\quad$ | $\quad A_2\quad$ | $\quad A_3\quad$ | $\quad Output\space y$ |
| --- | --- | --- | --- | --- |
| $\textbf{x}_1$ | 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 |
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.