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sklearn.preprocessing._data.RobustScaler

sklearn.preprocessing._data.RobustScaler

Visibility: public Uploaded 25-03-2021 by Pieter Gijsbers sklearn==0.23.1 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.1
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Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range 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. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results. .. versionadded:: 0.17

Parameters

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 returned.default: true
quantile_rangeDefault: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR Quantile range used to calculate ``scale_`` .. versionadded:: 0.18default: [25.0, 75.0]
with_centeringIf True, center the data before scaling This will cause ``transform`` to 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: true
with_scalingIf True, scale the data to interquartile rangedefault: true

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