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

sklearn.datasets.load_digits(n_class=10, return_X_y=False)

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

sklearn.datasets.fetch_california_housing(data_home=None, download_if_missing=True)

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sklearn.preprocessing.normalize()
  • References/Python/scikit-learn/API Reference/preprocessing

sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False)

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exceptions.EfficiencyWarning
  • References/Python/scikit-learn/API Reference/exceptions

class sklearn.exceptions.EfficiencyWarning

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exceptions.NonBLASDotWarning
  • References/Python/scikit-learn/API Reference/exceptions

class sklearn.exceptions.NonBLASDotWarning

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Varying regularization in Multi-layer Perceptron
  • References/Python/scikit-learn/Examples/Neural Networks

A comparison of different values for regularization parameter ?alpha? on synthetic datasets. The plot shows that different alphas yield different

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Gaussian Mixture Model Ellipsoids
  • References/Python/scikit-learn/Examples/Gaussian Mixture Models

Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation (GaussianMixture class) and Variational Inference

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Plot Ridge coefficients as a function of the regularization
  • References/Python/scikit-learn/Examples/Generalized Linear Models

Shows the effect of collinearity in the coefficients of an estimator.

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Label Propagation learning a complex structure
  • References/Python/scikit-learn/Examples/Semi Supervised Classification

Example of LabelPropagation learning a complex internal structure to demonstrate ?manifold learning?. The outer circle should be labeled ?red

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Test with permutations the significance of a classification score
  • References/Python/scikit-learn/Examples/Feature Selection

In order to test if a classification score is significative a technique in repeating the classification procedure after randomizing

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