========================= Putting it all together ========================= .. Imports >>> import numpy as np Pipelining ============ We have seen that some estimators can transform data and that some estimators can predict variables. We can also create combined estimators: .. image:: ../../auto_examples/images/plot_digits_pipe_001.png :target: ../../auto_examples/plot_digits_pipe.html :scale: 65 :align: right .. literalinclude:: ../../auto_examples/plot_digits_pipe.py :lines: 26-66 Face recognition with eigenfaces ================================= The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", also known as LFW_: http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB) .. _LFW: http://vis-www.cs.umass.edu/lfw/ .. literalinclude:: ../../auto_examples/applications/face_recognition.py .. |prediction| image:: ../../images/plot_face_recognition_1.png :scale: 50 .. |eigenfaces| image:: ../../images/plot_face_recognition_2.png :scale: 50 .. list-table:: :class: centered * - |prediction| - |eigenfaces| * - **Prediction** - **Eigenfaces** Expected results for the top 5 most represented people in the dataset:: precision recall f1-score support Gerhard_Schroeder 0.91 0.75 0.82 28 Donald_Rumsfeld 0.84 0.82 0.83 33 Tony_Blair 0.65 0.82 0.73 34 Colin_Powell 0.78 0.88 0.83 58 George_W_Bush 0.93 0.86 0.90 129 avg / total 0.86 0.84 0.85 282 Open problem: Stock Market Structure ===================================== Can we predict the variation in stock prices for Google over a given time frame? :ref:`stock_market`