Suppose you want ID3 to decide whether the weather is amenable to playing baseball. The target classification is "should we play baseball?" which can be yes or no. The weather attributes are outlook and wind speed. They can have the following values: outlook (sunny, overcast, rain ) wind - (weak, strong ) Examples of set S are (assume that all the examples in table below occur with equal probability) Dav Outlook Wind Weak Strong Weak eak Strong Strong ball logz I Yes D1 D2 D3 D4 D5 D6 Sunny Sunny Overcast 1.5850 2.0000 Yes Yes Overcast No 2.5850 Rain You need to find which attribute (outlook or wind) will be the root node in your decision tree. Use the log values in the table. Make sure to show your step by step work Recall the logarithm rule: log, (x/y)- logo (x)-log, (y) Suppose you want ID3 to decide whether the weather is amenable to playing baseball. The target classification is "should we play baseball?" which can be yes or no. The weather attributes are outlook and wind speed. They can have the following values: outlook (sunny, overcast, rain ) wind - (weak, strong ) Examples of set S are (assume that all the examples in table below occur with equal probability) Dav Outlook Wind Weak Strong Weak eak Strong Strong ball logz I Yes D1 D2 D3 D4 D5 D6 Sunny Sunny Overcast 1.5850 2.0000 Yes Yes Overcast No 2.5850 Rain You need to find which attribute (outlook or wind) will be the root node in your decision tree. Use the log values in the table. Make sure to show your step by step work Recall the logarithm rule: log, (x/y)- logo (x)-log, (y)