no Used in Decision Trees: Outlook (X1) Temperature (X2) Humidity (X) Play Tennis? (Y) sunny hot high overcast hot high
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no Used in Decision Trees: Outlook (X1) Temperature (X2) Humidity (X) Play Tennis? (Y) sunny hot high overcast hot high
no Used in Decision Trees: Outlook (X1) Temperature (X2) Humidity (X) Play Tennis? (Y) sunny hot high overcast hot high ves rain mild high yes rain cool normal yes sunny mild high sunny mild normal ves rain mild normal yes overcast hot normal ves 1. Using the dataset above, calculate the mutual information for each feature (X1, X2, X3) to determine the root node for a Decision Tree trained on the above data. 110 • What is I(Y:X;)? • What is I(Y; X2)? 0.061 . What is I(Y; X;)? 0.311 • What feature should be split on at the root node? 2. Calculate what the next split should be. 3. Draw the resulting tree.
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