sklearn.preprocessing
.scale¶
- sklearn.preprocessing.scale(X, *, axis=0, with_mean=True, with_std=True, copy=True)[source]¶
Standardize a dataset along any axis.
Center to the mean and component wise scale to unit variance.
Read more in the User Guide.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The data to center and scale.
- axisint, default=0
axis used to compute the means and standard deviations along. If 0, independently standardize each feature, otherwise (if 1) standardize each sample.
- with_meanbool, default=True
If True, center the data before scaling.
- with_stdbool, default=True
If True, scale the data to unit variance (or equivalently, unit standard deviation).
- copybool, default=True
set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSC matrix and if axis is 1).
- Returns:
- X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)
The transformed data.
See also
StandardScaler
Performs scaling to unit variance using the Transformer API (e.g. as part of a preprocessing
Pipeline
).
Notes
This implementation will refuse to center scipy.sparse matrices since it would make them non-sparse and would potentially crash the program with memory exhaustion problems.
Instead the caller is expected to either set explicitly
with_mean=False
(in that case, only variance scaling will be performed on the features of the CSC matrix) or to callX.toarray()
if he/she expects the materialized dense array to fit in memory.To avoid memory copy the caller should pass a CSC matrix.
NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation.
We use a biased estimator for the standard deviation, equivalent to
numpy.std(x, ddof=0)
. Note that the choice ofddof
is unlikely to affect model performance.For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
Warning
Risk of data leak
Do not use
scale
unless you know what you are doing. A common mistake is to apply it to the entire data before splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend usingStandardScaler
within a Pipeline in order to prevent most risks of data leaking:pipe = make_pipeline(StandardScaler(), LogisticRegression())
.