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Scale features using statistics that are robust to outliers. This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.RobustScaler`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This Scaler removes the median and scales the data according to the Interquartile Range (IQR). 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 (or each sample, depending on the `axis` argument) 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 Read more in the :ref:`User Guide <preprocessing_scaler>`. **Parameters** with_centering : boolean, True by default 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_scaling : boolean, True by default If True, scale the data to interquartile range. copy : boolean, optional, default is 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. **Attributes** ``center_`` : array of floats The median value for each feature in the training set. ``scale_`` : array of floats The (scaled) interquartile range for each feature in the training set. .. versionadded:: 0.17 *scale_* attribute. See also :class:`sklearn.preprocessing.StandardScaler` to perform centering and scaling using mean and variance. :class:`sklearn.decomposition.RandomizedPCA` with `whiten=True` to further remove the linear correlation across features. **Notes** See examples/preprocessing/plot_robust_scaling.py for an example. http://en.wikipedia.org/wiki/Median_(statistics) http://en.wikipedia.org/wiki/Interquartile_range














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_train_seq List of tuples: 

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supported_dtypes Supported dtypes 

Scale features using statistics that are robust to outliers. This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.RobustScaler`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This Scaler removes the median and scales the data according to the Interquartile Range (IQR). 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 (or each sample, depending on the `axis` argument) 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 Read more in the :ref:`User Guide <preprocessing_scaler>`. **Parameters** with_centering : boolean, True by default 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_scaling : boolean, True by default If True, scale the data to interquartile range. copy : boolean, optional, default is 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. **Attributes** ``center_`` : array of floats The median value for each feature in the training set. ``scale_`` : array of floats The (scaled) interquartile range for each feature in the training set. .. versionadded:: 0.17 *scale_* attribute. See also :class:`sklearn.preprocessing.StandardScaler` to perform centering and scaling using mean and variance. :class:`sklearn.decomposition.RandomizedPCA` with `whiten=True` to further remove the linear correlation across features. **Notes** See examples/preprocessing/plot_robust_scaling.py for an example. http://en.wikipedia.org/wiki/Median_(statistics) http://en.wikipedia.org/wiki/Interquartile_range




Center and scale the data This node has been automatically generated by wrapping the sklearn.preprocessing.data.RobustScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters



Compute the median and quantiles to be used for scaling. This node has been automatically generated by wrapping the sklearn.preprocessing.data.RobustScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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