What is the difference between decision tree bias and variance in practice?

Decision Trees typically have low bias and high variance, meaning they fit training data well but generalize inconsistently.

Bias refers to error from overly simplistic assumptions, while variance refers to sensitivity to training data. Decision Trees can perfectly fit complex patterns (low bias), but small data changes lead to different structures (high variance). Techniques like pruning reduce variance but increase bias, illustrating the tradeoff.