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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 |202314mData MiningBuild a decision tree using the training data in the table given below. Divide the height attribute into ranges as follows : \{0, 1.6\], (1.6, 1.7\], (1.7, 1.8\], (1.8, 1.9\], (1.9, 2.0\], (2.0, 5.0\] | Gender | Height (m) | Class | |:---:|:---:|:---:| | F | 1.6 | Short | | M | 2 | Tall | | F | 1.9 | Medium | | F | 1.88 | Medium | | F | 1.7 | Short | | M | 1.85 | Medium | | F | 1.6 | Short | | M | 1.7 | Short | | M | 2.2 | Tall | | M | 2.1 | Tall | | F | 1.8 | Medium | | M | 1.95 | Medium | | F | 1.9 | Medium | | F | 1.8 | Medium | | F | 1.75 | Medium |20218m
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