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  1. doc/presentations.rst

      by `Jake Vanderplas`_ at the 2012 PyData workshop at Google
    
        Interactive demonstration of some scikit-learn features. 75 minutes.
    
    - `scikit-learn tutorial <https://www.youtube.com/watch?v=cHZONQ2-x7I>`_ by `Jake Vanderplas`_ at PyData NYC 2012
    
        Presentation using the online tutorial, 45 minutes.
    
    
    Plain Text
    - Registered: 2020-09-18 09:24
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  2. .mailmap

    Jacob Schreiber <******@****.***>
    Jacob Schreiber <******@****.***> <******@****.***>
    Jake VanderPlas <vanderplas@astro.washington.edu> <******@****.***>
    Jake VanderPlas <vanderplas@astro.washington.edu> <******@****.***>
    Jake VanderPlas <vanderplas@astro.washington.edu> <vanderplas@astro.washington.edu>
    James Bergstra <******@****.***>
    Jaques Grobler <******@****.***> <******@****.***>
    Plain Text
    - Registered: 2020-09-18 09:24
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  3. examples/gaussian_process/plot_gpr_noisy_targets.py

    Note that the parameter ``alpha`` is applied as a Tikhonov
    regularization of the assumed covariance between the training points.
    """
    print(__doc__)
    
    # Author: Vincent Dubourg <******@****.***>
    #         Jake Vanderplas <vanderplas@astro.washington.edu>
    #         Jan Hendrik Metzen <******@****.***>s
    # License: BSD 3 clause
    
    import numpy as np
    from matplotlib import pyplot as plt
    
    Python
    - Registered: 2020-09-18 09:24
    - Last Modified: 2019-02-08 12:13
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  4. examples/manifold/plot_compare_methods.py

    the distances in the original high-dimensional space, unlike other
    manifold-learning algorithms, it does not seeks an isotropic
    representation of the data in the low-dimensional space.
    """
    
    # Author: Jake Vanderplas -- <vanderplas@astro.washington.edu>
    
    print(__doc__)
    
    from collections import OrderedDict
    from functools import partial
    from time import time
    
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    Python
    - Registered: 2020-09-18 09:24
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  5. doc/authors_emeritus.rst

    - Wei Li
    - Paolo Losi
    - Gilles Louppe
    - Vincent Michel
    - Jarrod Millman
    - Alexandre Passos
    - Fabian Pedregosa
    - Peter Prettenhofer
    - (Venkat) Raghav, Rajagopalan
    - Jacob Schreiber
    - Jake Vanderplas
    - David Warde-Farley
    Plain Text
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  6. sklearn/datasets/_species_distributions.py

    :ref:`examples/applications/plot_species_distribution_modeling.py
    <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
    """
    
    # Authors: Peter Prettenhofer <******@****.***>
    #          Jake Vanderplas <vanderplas@astro.washington.edu>
    #
    # License: BSD 3 clause
    
    from io import BytesIO
    from os import makedirs, remove
    from os.path import exists
    
    import logging
    import numpy as np
    
    import joblib
    
    Python
    - Registered: 2020-09-18 09:24
    - Last Modified: 2020-06-15 13:44
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  7. examples/linear_model/plot_polynomial_interpolation.py

    using a pipeline to add non-linear features. Kernel methods extend this idea
    and can induce very high (even infinite) dimensional feature spaces.
    """
    print(__doc__)
    
    # Author: Mathieu Blondel
    #         Jake Vanderplas
    # License: BSD 3 clause
    
    import numpy as np
    import matplotlib.pyplot as plt
    
    from sklearn.linear_model import Ridge
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.pipeline import make_pipeline
    Python
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  8. doc/modules/density.rst

    .. _density_estimation:
    
    ==================
    Density Estimation
    ==================
    .. sectionauthor:: Jake Vanderplas <vanderplas@astro.washington.edu>
    
    Density estimation walks the line between unsupervised learning, feature
    engineering, and data modeling.  Some of the most popular and useful
    density estimation techniques are mixture models such as
    Gaussian Mixtures (:class:`~sklearn.mixture.GaussianMixture`), and
    Plain Text
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  9. examples/applications/plot_species_distribution_modeling.py

       S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
       190:231-259, 2006.
    """
    
    # Authors: Peter Prettenhofer <******@****.***>
    #          Jake Vanderplas <vanderplas@astro.washington.edu>
    #
    # License: BSD 3 clause
    
    from time import time
    
    import numpy as np
    import matplotlib.pyplot as plt
    
    from sklearn.utils import Bunch
    Python
    - Registered: 2020-09-18 09:24
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  10. doc/whats_new/older_versions.rst

    |center-div| |banner2| |banner1| |banner3| |end-div|
    
    Changelog
    ---------
    
    - New :ref:`manifold` module by `Jake Vanderplas`_ and
      `Fabian Pedregosa`_.
    
    - New :ref:`Dirichlet Process <dirichlet_process>` Gaussian Mixture
      Model by `Alexandre Passos`_
    
    - :ref:`neighbors` module refactoring by `Jake Vanderplas`_ :
      general refactoring, support for sparse matrices in input, speed and
    Plain Text
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