77 lines
1.8 KiB
ReStructuredText
77 lines
1.8 KiB
ReStructuredText
=========================
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Putting it all together
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=========================
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.. Imports
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>>> import numpy as np
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Pipelining
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============
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We have seen that some estimators can transform data and that some estimators
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can predict variables. We can also create combined estimators:
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.. image:: /auto_examples/images/sphx_glr_plot_digits_pipe_001.png
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:target: ../../auto_examples/plot_digits_pipe.html
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:scale: 65
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:align: right
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.. literalinclude:: ../../auto_examples/plot_digits_pipe.py
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:lines: 23-63
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Face recognition with eigenfaces
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=================================
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The dataset used in this example is a preprocessed excerpt of the
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"Labeled Faces in the Wild", also known as LFW_:
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http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
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.. _LFW: http://vis-www.cs.umass.edu/lfw/
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.. literalinclude:: ../../auto_examples/applications/plot_face_recognition.py
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.. |prediction| image:: ../../images/plot_face_recognition_1.png
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:scale: 50
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.. |eigenfaces| image:: ../../images/plot_face_recognition_2.png
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:scale: 50
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.. list-table::
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:class: centered
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*
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- |prediction|
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- |eigenfaces|
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*
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- **Prediction**
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- **Eigenfaces**
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Expected results for the top 5 most represented people in the dataset::
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precision recall f1-score support
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Gerhard_Schroeder 0.91 0.75 0.82 28
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Donald_Rumsfeld 0.84 0.82 0.83 33
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Tony_Blair 0.65 0.82 0.73 34
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Colin_Powell 0.78 0.88 0.83 58
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George_W_Bush 0.93 0.86 0.90 129
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avg / total 0.86 0.84 0.85 282
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Open problem: Stock Market Structure
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=====================================
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Can we predict the variation in stock prices for Google over a given time frame?
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:ref:`stock_market`
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