👉 In artificial intelligence and machine learning, overpruning is a technique used to optimize the performance of a model by reducing its complexity. This technique involves pruning certain layers in the neural network or deep learning models to reduce the number of parameters that are being trained on. Overpruning can be useful when the model has too many parameters (i.e., it's too complex) and not enough data points to train effectively. It is often applied during training, before making predictions, to avoid overfitting
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What is the definition of Overprune? 🙋
👉 Overpruning is a common practice in machine learning, particularly in deep learning models. It refers to selecting a small subset of features or training data for the model, which can lead to overfitting and reduced generalization ability. In simple terms, overpruning involves selecting a few "features" (usually high-dimensional) from the input data set, rather than using all possible combinations. This means that the model is trained on a very small subset of the data, which may not be representative
