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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 |202014mData MiningBuild a decision tree using the training data in the table given below. Divide the height attribute into ranges as follows: [0, 1.6], \quad [1.6, 1.7], \quad [1.7, 1.8], \quad [1.8, 1.9], \quad [1.9, 2.0], \quad [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 |20237mData 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 |20218mArtificial intelligenceConstruct a simple decision tree from the given dataset: | Weather | Temp | Play? | |---|---|---| | Sunny | Hot | No | | Sunny | Cool | Yes | | Rainy | Cool | Yes | | Rainy | Hot | No | Explain your attribute selection criteria.20247m
PreviousExplain how the linear maximum-margin classifier can be modified to be non-linear and to allow misclassification of the training examples. Give a derivation of the modified optimization problem needed for training. You need only derive a statement of the primal optimization problem.NextExplain the model of over-fitting and under-fitting. Give the example of over-fitting and under-fitting in two-dimensional data.
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