1.6.
  • References/Python/scikit-learn/Guide

sklearn.neighbors

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sklearn.datasets.load_lfw_pairs()
  • References/Python/scikit-learn/API Reference/datasets

Warning DEPRECATED

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sklearn.neighbors.radius_neighbors_graph()
  • References/Python/scikit-learn/API Reference/neighbors

sklearn.neighbors.radius_neighbors_graph(X, radius, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False

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gaussian_process.kernels.Kernel
  • References/Python/scikit-learn/API Reference/gaussian_process

class sklearn.gaussian_process.kernels.Kernel

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sklearn.datasets.clear_data_home()
  • References/Python/scikit-learn/API Reference/datasets

sklearn.datasets.clear_data_home(data_home=None)

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cross_validation.StratifiedKFold()
  • References/Python/scikit-learn/API Reference/cross_validation

Warning DEPRECATED

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Linear and Quadratic Discriminant Analysis with confidence ellipsoid
  • References/Python/scikit-learn/Examples/Classification

Plot the confidence ellipsoids of each class and decision boundary print(__doc__)

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Putting it all together
  • References/Python/scikit-learn/Tutorials

Pipelining We have seen that some estimators can transform data and that some estimators can predict variables. We can also create combined estimators:

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sklearn.datasets.load_sample_image()
  • References/Python/scikit-learn/API Reference/datasets

sklearn.datasets.load_sample_image(image_name)

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Principal components analysis
  • References/Python/scikit-learn/Examples/Decomposition

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat.

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