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Update regression.py added root_mean_square_deviation method #7531

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Update regression.py added root_mean_square_deviation method #7531

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54 changes: 54 additions & 0 deletions sklearn/metrics/regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,6 +241,60 @@ def mean_squared_error(y_true, y_pred,
return np.average(output_errors, weights=multioutput)


def root_mean_square_deviation(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Root mean squared deviation

Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.

y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.

sample_weight : array-like of shape = (n_samples), optional
Sample weights.

multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.

'raw_values' :
Returns a full set of errors in case of multioutput input.

'uniform_average' :
Errors of all outputs are averaged with uniform weight.

Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.

Examples
--------
>>> from sklearn.metrics import root_mean_squared_deviation
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> root_mean_squared_deviation(y_true, y_pred)
0.612...
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> root_mean_squared_deviation(y_true, y_pred) # doctest: +ELLIPSIS
0.841...
>>> root_mean_squared_deviation(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([ 0.64549722, 1. ])
>>> root_mean_squared_deviation(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.908...
"""

return np.sqrt(mean_squared_error(y_true, y_pred, sample_weight, multioutput))

def median_absolute_error(y_true, y_pred):
"""Median absolute error regression loss

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