Pixel importances with a parallel forest of trees
  • References/Python/scikit-learn/Examples/Ensemble methods

This example shows the use of forests of trees to evaluate the importance of the pixels in an image classification task (faces). The hotter

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

Warning DEPRECATED

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sklearn.metrics.precision_recall_fscore_support()
  • References/Python/scikit-learn/API Reference/metrics

sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None

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base.ClusterMixin
  • References/Python/scikit-learn/API Reference/base

class sklearn.base.ClusterMixin

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sklearn.covariance.shrunk_covariance()
  • References/Python/scikit-learn/API Reference/covariance

sklearn.covariance.shrunk_covariance(emp_cov, shrinkage=0.1)

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manifold.TSNE()
  • References/Python/scikit-learn/API Reference/manifold

class sklearn.manifold.TSNE(n_components=2, perplexity=30.0, early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000, n_iter_without_progress=30

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

sklearn.neighbors

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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.feature_extraction.image.reconstruct_from_patches_2d()
  • References/Python/scikit-learn/API Reference/feature_extraction

sklearn.feature_extraction.image.reconstruct_from_patches_2d(patches, image_size)

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covariance.EllipticEnvelope()
  • References/Python/scikit-learn/API Reference/covariance

class sklearn.covariance.EllipticEnvelope(store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1

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