{"flow":{"id":"5298","uploader":"1159","name":"TEST0023563da6sklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"1","external_version":"openml==0.15.0,sklearn==0.23.1","description":"Standardize features by removing the mean and scaling to unit variance\n\nThe standard score of a sample `x` is calculated as:\n\n z = (x - u) \/ s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 a...","upload_date":"2024-10-17T15:56:07","language":"English","dependencies":"sklearn==0.23.1\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"copy","data_type":"boolean","default_value":"true","description":"If False, try to avoid a copy and do inplace scaling instead\n This is not guaranteed to always work inplace; e.g. if the data is\n not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n returned"},{"name":"with_mean","data_type":"boolean","default_value":"true","description":"If True, center the data before scaling\n This does not work (and will raise an exception) when attempted on\n sparse matrices, because centering them entails building a dense\n matrix which in common use cases is likely to be too large to fit in\n memory"},{"name":"with_std","data_type":"boolean","default_value":"true","description":"If True, scale the data to unit variance (or equivalently,\n unit standard deviation)."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.23.1"]}}