👉 Over-normalized data refers to a set of data that is slightly less normalized than the original dataset. This means that some values are more concentrated than others, and the overall distribution of the data may not be as spread out as it would be if all values were equally likely. For example, consider a dataset where there are many outliers (values greater than 3 times the mean) in one group compared to the rest of the dataset. This can make the dataset look more "over-normalized"
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What is the definition of Overnormalize? 🙋
👉 Overnormalization is a technique used in machine learning and data science to normalize data before training neural networks. It involves scaling all the features of a dataset to have zero mean and unit variance, which can help improve the performance of these models by reducing noise and bias. For example, if you are using a neural network with many input features, you might want to normalize them so that each feature has an equal weight in the final output. Overnormalization helps achieve this by dividing each feature by its
