How does decision tree pruning help in reducing overfitting?

In machine learning, decision tree pruning is an important technique that reduces overfitting while improving the model's ability to generalize new data. Overfitting is when a decision-tree captures noise from the training dataset and becomes too complex. It also becomes highly specific to that data. Unpruned trees may achieve 100% accuracy in the training data, but perform poorly when compared to unseen data. By simplifying the structure of the tree, pruning can address this problem. Data Science Course in Pune

There are two types of pruning, pre-pruning or post-pruning. Pre-pruning (also known as early stopping) involves setting limits on the growth of the tree during training. These constraints can include limiting tree depth, defining the minimum number of split samples, or defining an information gain threshold. Pre-pruning can prevent excessive branching by limiting the growth of trees. This can reduce overfitting. Pre-pruning can have a downside, however, as it may stop the tree too soon, missing important patterns.
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