Pruning

Pruning is a technique used in machine learning and artificial neural networks to reduce the size of the model by removing unnecessary or redundant features. It involves removing the weights and connections between neurons that have little or no impact on the output of the model. Pruning can be done during training or after training, and it can be applied to different types of models, including decision trees, random forests, and deep neural networks. Pruning can improve the performance of the model by reducing overfitting, increasing generalization, and making the model more interpretable. However, pruning can also lead to underfitting if too many features are removed, and it can be computationally expensive, especially for large models.
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