Sparse inverse covariance estimation
  • References/Python/scikit-learn/Examples/Covariance estimation

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g

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3.1.
  • References/Python/scikit-learn/Guide

Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat

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cluster.MeanShift()
  • References/Python/scikit-learn/API Reference/cluster

class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=1)

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Pipelining
  • References/Python/scikit-learn/Examples/General examples

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to

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feature_selection.SelectKBest()
  • References/Python/scikit-learn/API Reference/feature_selection

class sklearn.feature_selection.SelectKBest(score_func=, k=10)

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3.2.
  • References/Python/scikit-learn/Guide

Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of

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

sklearn.datasets.fetch_lfw_people(data_home=None, funneled=True, resize=0.5, min_faces_per_person=0, color=False, slice_=(slice(70

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Online learning of a dictionary of parts of faces
  • References/Python/scikit-learn/Examples/Clustering

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

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sklearn.ensemble.partial_dependence.plot_partial_dependence()
  • References/Python/scikit-learn/API Reference/ensemble

sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None

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

sklearn.metrics.calinski_harabaz_score(X, labels)

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