If a feature has a variance that is orders of magnitude larger Machines or the L1 and L2 regularizers of linear models) assume thatĪll features are centered around 0 and have variance in the same Gaussian with 0 mean and unit variance).įor instance many elements used in the objective function ofĪ learning algorithm (such as the RBF kernel of Support Vector Individual features do not more or less look like standard normallyĭistributed data (e.g. Machine learning estimators: they might behave badly if the Standardization of a dataset is a common requirement for many Standard deviation are then stored to be used on later data using The relevant statistics on the samples in the training set. Where u is the mean of the training samples or zero if with_mean=False,Īnd s is the standard deviation of the training samples or one ifĬentering and scaling happen independently on each feature by computing
