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Standardize features by removing the mean and scaling to unit variance This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.StandardScaler`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 feature 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 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected. This scaler can also be applied to sparse CSR or CSC matrices by passing `with_mean=False` to avoid breaking the sparsity structure of the data. Read more in the :ref:`User Guide <preprocessing_scaler>`. **Parameters** with_mean : 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_std : boolean, True by default If True, scale the data to unit variance (or equivalently, unit standard deviation). copy : boolean, optional, default 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** ``scale_`` : ndarray, shape (n_features,) Per feature relative scaling of the data. .. versionadded:: 0.17 *scale_* is recommended instead of deprecated *std_*. ``mean_`` : array of floats with shape [n_features] The mean value for each feature in the training set. ``var_`` : array of floats with shape [n_features] The variance for each feature in the training set. Used to compute `scale_` ``n_samples_seen_`` : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. See also :func:`sklearn.preprocessing.scale` to perform centering and scaling without using the ``Transformer`` object oriented API :class:`sklearn.decomposition.RandomizedPCA` with `whiten=True` to further remove the linear correlation across features.














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_train_seq List of tuples: 

dtype dtype 

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output_dim Output dimensions 

supported_dtypes Supported dtypes 

Standardize features by removing the mean and scaling to unit variance This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.StandardScaler`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 feature 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 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected. This scaler can also be applied to sparse CSR or CSC matrices by passing `with_mean=False` to avoid breaking the sparsity structure of the data. Read more in the :ref:`User Guide <preprocessing_scaler>`. **Parameters** with_mean : 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_std : boolean, True by default If True, scale the data to unit variance (or equivalently, unit standard deviation). copy : boolean, optional, default 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** ``scale_`` : ndarray, shape (n_features,) Per feature relative scaling of the data. .. versionadded:: 0.17 *scale_* is recommended instead of deprecated *std_*. ``mean_`` : array of floats with shape [n_features] The mean value for each feature in the training set. ``var_`` : array of floats with shape [n_features] The variance for each feature in the training set. Used to compute `scale_` ``n_samples_seen_`` : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. See also :func:`sklearn.preprocessing.scale` to perform centering and scaling without using the ``Transformer`` object oriented API :class:`sklearn.decomposition.RandomizedPCA` with `whiten=True` to further remove the linear correlation across features.




Perform standardization by centering and scaling This node has been automatically generated by wrapping the sklearn.preprocessing.data.StandardScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters



Compute the mean and std to be used for later scaling. This node has been automatically generated by wrapping the sklearn.preprocessing.data.StandardScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
y: Passthrough for Pipeline compatibility.

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