.. _example_preprocessing_plot_robust_scaling.py: ========================================================= Robust Scaling on Toy Data ========================================================= Making sure that each Feature has approximately the same scale can be a crucial preprocessing step. However, when data contains outliers, :class:`StandardScaler ` can often be mislead. In such cases, it is better to use a scaler that is robust against outliers. Here, we demonstrate this on a toy dataset, where one single datapoint is a large outlier. .. image:: images/\plot_robust_scaling_001.png :align: center **Script output**:: Testset accuracy using standard scaler: 0.545 Testset accuracy using robust scaler: 0.700 **Python source code:** :download:`plot_robust_scaling.py ` .. literalinclude:: plot_robust_scaling.py :lines: 18- **Total running time of the example:** 0.60 seconds ( 0 minutes 0.60 seconds)