18807 18336 sklearn.preprocessing.data.StandardScaler sklearn.StandardScaler sklearn.preprocessing.data.StandardScaler 42 openml==0.11.0,sklearn==0.18.1 Standardize features by removing the mean and scaling to unit variance Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using the `transform` method. Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual feature do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance). For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.... 2021-04-13T23:07:00 English sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 copy boolean true If False, try to avoid a copy and do inplace scaling instead This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. with_mean boolean false If True, center the data before scaling This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory with_std boolean true If True, scale the data to unit variance (or equivalently, unit standard deviation) openml-python python scikit-learn sklearn sklearn_0.18.1