ml decision tree

Compared to other models, decision trees have many advantages:

• Features may be numeric or categorical: Samples can be split using either numerical features (by splitting a range) or categorical ones without us having to encode either.
• Reduced need for data preparation: Decision splits are not sensitive to data ranges or size. Many other models (for example, neural networks) require data to be normalized to unit ranges.
• Interpretability: As shown previously, it’s straightforward to interpret the predictions made by a tree. Interpretability is valuable in contexts where a prediction must be explained to decision-makers

disadvantages associated with decision trees:

• Overfitting: Decision trees are very prone to overfitting. Setting the correct hyperparameters is essential when fitting decision trees. Overfitting in decision trees will be discussed in detail later.
• Poor extrapolation: Decision trees are poor at extrapolation since their predictions are not continuous and are effectively bounded by the training data.
• Unbalanced data: When fitting a tree on unbalanced data, the high-frequency classes dominate the predictions. Data needs to be prepared to remove imbalances.