diff --git a/sklearn/metrics/regression.py b/sklearn/metrics/regression.py
index af3a02d6f33f9..45501d06f5b85 100644
--- a/sklearn/metrics/regression.py
+++ b/sklearn/metrics/regression.py
@@ -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