Description: Pruning is the process of removing unnecessary data or features to improve model performance. In the context of machine learning and artificial intelligence, pruning refers to reducing the complexity of a model by eliminating parameters or features that do not provide significant value to predictions. This process is crucial to avoid overfitting, where a model adapts too closely to training data and loses its ability to generalize to unseen data. Pruning can be applied at different levels, from removing irrelevant features during data preprocessing to simplifying complex model architectures. By reducing the dimensionality of the feature space, computational efficiency is improved, and training time is accelerated, resulting in faster and more effective models. Additionally, pruning contributes to model interpretability, making it easier to identify the most influential features in the model’s decisions. In the field of deep learning, pruning has become an essential technique for optimizing neural networks, allowing them to maintain high performance while being reduced in size and complexity.
