DOC correct typo in StandardScaler documentation
@@ -654,7 +654,7 @@ class StandardScaler(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
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
+ than 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
