Question 6What is a disadvantage of decision trees?1 pointScaling is required.They tend to overfit.They can get too large.They are difficult to interpret.

Understanding Decision Trees

Decision trees are a popular tool in machine learning for classification and regression tasks. However, like any algorithm, they have their disadvantages.

Analyzing the Options

1. Scaling is required: Decision trees do not require feature scaling because they are not sensitive to the magnitude of the features used for splitting.

2. They tend to overfit: This is a well-known drawback of decision trees. They can create a model that is too complex and fits the noise in the training data rather than the underlying distribution.

3. They can get too large: Indeed, decision trees can become excessively complex if not properly pruned, but this is often a symptom of overfitting.

4. They are difficult to interpret: This option is generally considered incorrect. One of the advantages of decision trees is that they are quite easy to interpret compared to other complex models.

Conclusion

The most significant disadvantage of decision trees among the options is that they tend to overfit. This can lead to poor performance on unseen data, hence making it a critical concern when using decision trees for predictive modeling.