linear_model.Ridge()
  • References/Python/scikit-learn/API Reference/linear_model

class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto',

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linear_model.TheilSenRegressor()
  • References/Python/scikit-learn/API Reference/linear_model

class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,

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kernel_ridge.KernelRidge()
  • References/Python/scikit-learn/API Reference/kernel_ridge

class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)

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Scalability of Approximate Nearest Neighbors
  • References/Python/scikit-learn/Examples/Nearest Neighbors

This example studies the scalability profile of approximate 10-neighbors queries using the LSHForest with n_estimators=20 and

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ensemble.ExtraTreesRegressor()
  • References/Python/scikit-learn/API Reference/ensemble

class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1

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

sklearn.metrics.pairwise.paired_euclidean_distances(X, Y)

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FeatureHasher and DictVectorizer Comparison
  • References/Python/scikit-learn/Examples/Working with text documents

Compares FeatureHasher and DictVectorizer by using both to vectorize text documents. The example demonstrates syntax and speed only; it doesn

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Libsvm GUI
  • References/Python/scikit-learn/Examples/Examples based on real world datasets

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different

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Plot randomly generated classification dataset
  • References/Python/scikit-learn/Examples/Dataset examples

Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets

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random_projection.SparseRandomProjection()
  • References/Python/scikit-learn/API Reference/random_projection

class sklearn.random_projection.SparseRandomProjection(n_components='auto', density='auto', eps=0.1, dense_output=False

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