17424
1
sklearn.preprocessing.data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing.data.StandardScaler
36
openml==0.10.2,sklearn==0.21.3
Standardize features by removing the mean and scaling to unit variance
The standard score of a sample `x` is calculated as:
z = (x - u) / s
where `u` is the mean of the training samples or zero if `with_mean=False`,
and `s` is the standard deviation of the training samples or one if
`with_std=False`.
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 features 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 aroun...
2019-11-22T02:17:02
English
sklearn==0.21.3
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
true
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_0.21.3