<p>来自<a href="http://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html" rel="noreferrer">ScikitLearn site</a>:</p>
<blockquote>
<p><code>StandardScaler</code> removes the mean and scales the data to unit variance.
However, the outliers have an influence when computing the empirical
mean and standard deviation which shrink the range of the feature
values as shown in the left figure below. Note in particular that
because the outliers on each feature have different magnitudes, the
spread of the transformed data on each feature is very different: most
of the data lie in the [-2, 4] range for the transformed median income
feature while the same data is squeezed in the smaller [-0.2, 0.2]
range for the transformed number of households.</p>
<p>StandardScaler therefore cannot guarantee balanced feature scales in
the presence of outliers.</p>
<p><code>MinMaxScaler</code> rescales the data set such that all feature values are in
the range [0, 1] as shown in the right panel below. However, this
scaling compress all inliers in the narrow range [0, 0.005] for the
transformed number of households.</p>
</blockquote>