Intermediate
Artificial Intelligence & Machine Learning
Q70 / 100
What is the main practical tradeoff when increasing the depth of a decision tree?
Correct! Well done.
Incorrect.
The correct answer is B) Deeper trees can capture more complex patterns but are increasingly likely to overfit the training data and lose generalization to new data
B
Correct Answer
Deeper trees can capture more complex patterns but are increasingly likely to overfit the training data and lose generalization to new data
Explanation
As depth increases, a tree can fit training data (and its noise) more closely, increasing variance and the risk of overfitting. Techniques like pruning, setting a maximum depth, or requiring a minimum samples per leaf help balance fit and generalization.
Progress
70/100