OpenML

JavaScript is required to properly view the contents of this page!

Explore
- Data
- Task
- Flow
- Run
- Study
- Task type
- Measure
- People
Help
Blog
Contact
Please cite us

sklearn.preprocessing.data.StandardScaler

Visibility: public Uploaded 22-11-2019 by Jason sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 0 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Loading wiki

Help us complete this description Edit

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...

Parameters

copy	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	default: true
with_mean	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	default: true
with_std	If True, scale the data to unit variance (or equivalently, unit standard deviation).	default: true

0
Runs

List all runs

Parameter:

Rendering chart

Rendering table

Sign in

sklearn.preprocessing.data.StandardScaler

Parameters

0 Runs

A PHP Error was encountered

0
Runs