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Data MiningExplain the working of the Decision Tree method for classification.20257mMACHINE LEARNINGDiscuss various issues in Decision Tree Learning.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 |20218mData MiningDiscuss overfitting and underfitting in decision tree construction with suitable example.20227m
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.
