MNT Better error message for MinMaxScaler and sparse data
@@ -365,8 +365,8 @@ class MinMaxScaler(TransformerMixin, BaseEstimator):
" than maximum. Got %s." % str(feature_range))
if sparse.issparse(X):
- raise TypeError("MinMaxScaler does no support sparse input. "
- "You may consider to use MaxAbsScaler instead.")
+ raise TypeError("MinMaxScaler does not support sparse input. "
+ "Consider using MaxAbsScaler instead.")
X = check_array(X,
estimator=self, dtype=FLOAT_DTYPES,
