Meannormalization

Mean normalization is a data preprocessing technique used in machine learning and statistics. It is a method of feature scaling that centers the data around zero. This is achieved by subtracting the mean of each feature from all of its values. The formula for mean normalization is: X_normalized = X - mean(X). Unlike standardization, which also scales the data to have a unit variance, mean normalization only shifts the data distribution. The resulting values will have a mean of zero, but their standard deviation may not be one. This technique is useful when the algorithm is sensitive to the scale of the features but not necessarily their variance. For example, some gradient descent algorithms can converge faster when features are mean normalized. It is important to note that mean normalization assumes that the data is approximately normally distributed, although it can still be applied to other distributions. When applying mean normalization, it is crucial to calculate the mean from the training data and then use that same mean to normalize both the training and testing datasets to avoid data leakage.