. Outlook Temperature Humidity Windy Original - 1 Sunny Hot 2 Overcas: Hot 3 Rainy Mild Rainy Cool 5 Overcast Cool 6 Sunny Cool 7 Rainy Mild Sunny Mild 9 Overcast Mild 10 Overcast Hor Consider the class "Yes" as the positive class and "Not" as the negative class TRUE No FALSE YES FALSE No Normal TRUE No Normal TRUE VES Normal FALSE NO Normal FALSE High FALSE No High TRUE YES Normal FALSE YES 00 Suppose that the data set is used as a training set, calculate the information gain of the attribute "Windy" 14 points) Hint: entropy (p1.p2.pn) = pi fog 21-p2log P2 - on logon For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac). BI S Paragraph Arial 14px Outlook Temperature Humidity Windy Original Class (Play) 1 Sunny Hot High TRUE No 2 Overcast Hot High FALSE YES 3 Rainy Mild High FALSE No 4 Rainy Cool Normal TRUE No 5 Overcast Cool Normal TRUE YES 6 Sunny Cool Normal FALSE No 7 Rainy Mild Normal FALSE No 8 Sunny Mild High FALSE No 9 Overcast | Mild High TRUE YES 10 Overcast Hot Normal FALSE YES "Not" as the negative class e information gain of the attribute "Windy". [4 points) 6 Sunny Cool 7 Rainy Mild 8 Sunny Mild 9 Overcast Mild 10 Overcast Hot Consider the class "Yes" as the positive class and "Not" as the negative class Normal Normal High High Norm Suppose that the data set is used as a training set, calculate the information gain of the attribute "Windy Hint: entropy (p1,p2, ...,n) = pi log pl - p2log p2. ...-on log on For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac). B I U S Paragraph Arial 14px V