tree.DecisionTreeClassifier()
  • References/Python/scikit-learn/API Reference/tree

class sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1

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Parameter estimation using grid search with cross-validation
  • References/Python/scikit-learn/Examples/Model Selection

This examples shows how a classifier is optimized by cross-validation, which is done using the

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

sklearn.metrics.adjusted_rand_score(labels_true, labels_pred)

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Comparison of LDA and PCA 2D projection of Iris dataset
  • References/Python/scikit-learn/Examples/Decomposition

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width,

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neural_network.BernoulliRBM()
  • References/Python/scikit-learn/API Reference/neural_network

class sklearn.neural_network.BernoulliRBM(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)

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Recursive feature elimination
  • References/Python/scikit-learn/Examples/Feature Selection

A recursive feature elimination example showing the relevance of pixels in a digit classification task.

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Classification of text documents using sparse features
  • References/Python/scikit-learn/Examples/Working with text documents

This is an example showing how scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example uses

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2.6.
  • References/Python/scikit-learn/Guide

Many statistical problems require at some point the estimation of a population?s covariance matrix, which can be seen as an estimation of data set scatter plot shape.

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

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

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cross_decomposition.PLSRegression()
  • References/Python/scikit-learn/API Reference/cross_decomposition

class sklearn.cross_decomposition.PLSRegression(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)

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