(Solved): Question 2 You are given a training dataset \( D \) shown in the table below for a binary classifica ...

Question 2 You are given a training dataset \( D \) shown in the table below for a binary classification problem. \begin{table} \captionsetup{labelformat=empty} \caption{The class-labeled training dataset \( D \)} \begin{tabular}{|l|l|l|l|l|l|} \hline No. & Weather & Weekend & Housing & Company & Barbeque \\ \hline 1 & Winter & Yes & Bungalow & Family & Yes \\ \hline 2 & Summer & Yes & Villa & Friends & No \\ \hline 3 & Winter & No & Flat & Co-worker & Yes \\ \hline 4 & Autumn & Yes & Bungalow & Co-worker & No \\ \hline 5 & Winter & Yes & Bungalow & Friends & Yes \\ \hline 6 & Autumn & No & Villa & Family & Yes \\ \hline 7 & Summer & No & Flat & Friends & Yes \\ \hline 8 & Spring & No & Villa & Co-worker & No \\ \hline 9 & Spring & Yes & Bungalow & Family & Yes \\ \hline 10 & Winter & Yes & Flat & Friends & No \\ \hline 11 & Summer & No & Villa & Family & Yes \\ \hline 12 & Autumn & Yes & Flat & Co-worker & Yes \\ \hline \end{tabular} \end{table} The attributes are Weather, Weekend, Housing, Company, and Barbeque. The Barbeque attribute is the class label attribute (i.e., target feature) that has two different values \{Yes, No\}. Let \( C_{1}= \) Yes and \( C_{2}= \) No. Possible values of the attributes Weather, Weekend, Housing, and Company are \( \{ \) Autumn, Spring, Summer, Winter\}, \{Yes, No\}, \{Bungalow, Flat, Villa\}, and \{Coworker, Family, Friends \( \} \), respectively. Given the test instance \( \boldsymbol{X}=( \) Weather \( = \) Autumn, Weekend \( = \) Yes, Housing \( = \) Villa, Company \( = \) Co-worker). Use a naïve Bayes classifier to predict the given test instance \( \boldsymbol{X} \) ? Note: Use 4 digits after the floating point in your calculations. [Total mark: 50]