Question

Discuss issues of avoiding over fitting the data, handling continuous data and missing values in decision tree

Answer 1

Answer:

Explanation:

increased test set error. There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.

Answer 2

For taking steps to know about Data Science and Machine Learning, ... and how can we avoid over-fitting in decision trees: ... Maximum features to consider for split