medium-table7.txt

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|                                                                                  Advantages                                                                                  |                                                                       Disadvantages                                                                       |
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| Easy to Understand                                                                                                                                                           | Can show spurious accuracy if dataset is imbalanced towards a particular class. Fix: Use a representative/stratified sample for training                  |
| Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables.                       | Is prone to overfitting or lack of generalization. Fix: Use pruning (play with depth of tree) and minimum number of sample points required to split tree  |
| Data type is not a constraint: It can handle both numerical and categorical variables.                                                                                       |                                                                                                                                                           |
| Non-Parametric Method: Decision tree is considered to be a non-parametric method. This means that decision trees make no assumptions about the underlying data distribution. |                                                                                                                                                           |
| Non-linear relationships between parameters do not affect tree performance.                                                                                                  |                                                                                                                                                           |
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