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Data MiningDraw decision tree for the following data sets. Use entropy as a node selection mechanism: | Outlook | Temp (F) | Humidity | Windy | Class | |:---|:---|:---|:---|:---| | Rainy | Hot | High | False | No | | Rainy | Hot | High | True | No | | Overcast | Hot | High | False | Yes | | Sunny | Mild | High | False | Yes | | Sunny | Cool | Normal | False | Yes | | Sunny | Cool | Normal | True | No | | Overcast | Cool | Normal | True | Yes | | Rainy | Mild | High | False | No | | Rainy | Cool | Normal | False | Yes | | Sunny | Mild | Normal | False | Yes | | Rainy | Mild | Normal | True | Yes | | Overcast | Mild | High | True | Yes | | Overcast | Hot | Normal | False | Yes | | Sunny | Mild | High | True | No |202014mMACHINE LEARNINGApply the ID3 algorithm on the following data to draw a decision tree. Show the Information Gain at each split. ### Dataset | Outlook | Temperature | Humidity | Windy | PlayTennis | |-----------|-------------|----------|-------|-------------| | Sunny | Hot | High | False | No | | Sunny | Hot | High | True | No | | Overcast | Hot | High | False | Yes | | Rainy | Mild | High | False | Yes | | Rainy | Cool | Normal | False | Yes | | Rainy | Cool | Normal | True | No | | Overcast | Cool | Normal | True | Yes | | Sunny | Mild | High | False | No | | Sunny | Cool | Normal | False | Yes | | Rainy | Mild | Normal | False | Yes | | Sunny | Mild | Normal | True | Yes | | Overcast | Mild | High | True | Yes | | Overcast | Hot | Normal | False | Yes | | Rainy | Mild | High | True | No |202510mMACHINE LEARNINGFor the Sun Burn data set given below, construct a decision tree. | Name | Hair | Height | Weight | Location | Class | |--------|--------|----------|---------|----------|-------| | Sunita | Blonde | Average | Light | No | Yes | | Anita | Blonde | Tall | Average | Yes | No | | Kavita | Brown | Short | Average | Yes | No | | Sushma | Blonde | Short | Average | No | Yes | | Xavier | Red | Average | Heavy | No | Yes | | Balaji | Brown | Tall | Heavy | No | No | | Ramesh | Brown | Average | Heavy | No | No | | Sweta | Blonde | Short | Light | Yes | No |202314mMACHINE LEARNINGFollowing is a data set that contains two attributes, X and Y, and two class labels, ‘+’ and ‘–’. Each attribute can take three different values: 0, 1, or 2. The concept for the ‘+’ class is Y = 1 and the concept for the ‘–’ class is X = 0 ∨ X = 2.  (a) Build a decision tree on the data set. Does the tree capture the ‘+’ and ‘–’ concepts? (b) What are the accuracy, precision, recall and F1-measure of the decision tree? (Note that precision, recall, and F1-measure are defined with respect to the ‘+’ class.)202214m
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