.. _sphx_glr_auto_examples_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:: /auto_examples/preprocessing/images/sphx_glr_plot_robust_scaling_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Testset accuracy using standard scaler: 0.545 Testset accuracy using robust scaler: 0.705 | .. code-block:: python from __future__ import print_function print(__doc__) # Code source: Thomas Unterthiner # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.preprocessing import StandardScaler, RobustScaler # Create training and test data np.random.seed(42) n_datapoints = 100 Cov = [[0.9, 0.0], [0.0, 20.0]] mu1 = [100.0, -3.0] mu2 = [101.0, -3.0] X1 = np.random.multivariate_normal(mean=mu1, cov=Cov, size=n_datapoints) X2 = np.random.multivariate_normal(mean=mu2, cov=Cov, size=n_datapoints) Y_train = np.hstack([[-1]*n_datapoints, [1]*n_datapoints]) X_train = np.vstack([X1, X2]) X1 = np.random.multivariate_normal(mean=mu1, cov=Cov, size=n_datapoints) X2 = np.random.multivariate_normal(mean=mu2, cov=Cov, size=n_datapoints) Y_test = np.hstack([[-1]*n_datapoints, [1]*n_datapoints]) X_test = np.vstack([X1, X2]) X_train[0, 0] = -1000 # a fairly large outlier # Scale data standard_scaler = StandardScaler() Xtr_s = standard_scaler.fit_transform(X_train) Xte_s = standard_scaler.transform(X_test) robust_scaler = RobustScaler() Xtr_r = robust_scaler.fit_transform(X_train) Xte_r = robust_scaler.transform(X_test) # Plot data fig, ax = plt.subplots(1, 3, figsize=(12, 4)) ax[0].scatter(X_train[:, 0], X_train[:, 1], color=np.where(Y_train > 0, 'r', 'b')) ax[1].scatter(Xtr_s[:, 0], Xtr_s[:, 1], color=np.where(Y_train > 0, 'r', 'b')) ax[2].scatter(Xtr_r[:, 0], Xtr_r[:, 1], color=np.where(Y_train > 0, 'r', 'b')) ax[0].set_title("Unscaled data") ax[1].set_title("After standard scaling (zoomed in)") ax[2].set_title("After robust scaling (zoomed in)") # for the scaled data, we zoom in to the data center (outlier can't be seen!) for a in ax[1:]: a.set_xlim(-3, 3) a.set_ylim(-3, 3) plt.tight_layout() plt.show() # Classify using k-NN from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() knn.fit(Xtr_s, Y_train) acc_s = knn.score(Xte_s, Y_test) print("Testset accuracy using standard scaler: %.3f" % acc_s) knn.fit(Xtr_r, Y_train) acc_r = knn.score(Xte_r, Y_test) print("Testset accuracy using robust scaler: %.3f" % acc_r) **Total running time of the script:** (0 minutes 0.284 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_robust_scaling.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_robust_scaling.ipynb `