Bias-Variance Tradeoff:
Pruning in the context of decision trees is a technique used to reduce the size of the tree by removing sections of the tree that provide little power in classifying instances. The main goal of pruning is to enhance the model’s ability to generalize to new data by mitigating overfitting.
Maximum Tree Creation:
Overfitting:
Bias-Variance Tradeoff:
Pruning:
Enhanced Generalization:
Thus, pruning helps in reducing overfitting, balancing the bias-variance tradeoff, and ultimately enhancing the model’s ability to generalize from the training data to new, unseen data.