👉 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
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What is the definition of Overpruned? 🙋
👉 Overpruned in programming refers to a specific type of data structure that is designed for efficient use but can be difficult to understand and implement correctly. Overprunners are often used as a way to simplify code, especially in scenarios where the complexity of the problem or algorithm is high. In terms of algorithms, overpruned data structures may be more suitable than normal data structures like arrays because they have fewer operations available to them (such as insertion, deletion, and searching) which can make them
