Visibility: public Uploaded 10-01-2024 by Continuous Integration sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 threadpoolctl>=2.0.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_1.3.2
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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 :meth:`transform`. 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 around 0 ...


copyIf 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 returneddefault: true
with_meanIf 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 memorydefault: false
with_stdIf True, scale the data to unit variance (or equivalently, unit standard deviation).default: true


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