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plot_multi_metric_evaluation.rst.txt
label="%s (%s)" % (scorer, sample), ) best_index...sample_score_mean = results["mean_%s_%s" % (sample, scorer)] sample_score_std...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
plot_kmeans_digits.rst.txt
3f}s\t{:.0f}\t{:.3f}\t{:.3f}\t{:.3...centroids[:, 1], marker="x", s=169, linewidths=3, color="w",...scikit-learn.org/stable/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt -
plot_pca_iris.rst.txt
s=40, ) ax.set( title="First three...scikit-learn.org/stable/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt -
feature_extraction.rst.txt
constructor:: >>> def my_tokenizer(s): ... return s.split() ... >>> vectorizer...Using the ``TfidfTransformer``'s default settings, ``TfidfTransformer(norm='l2',...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
clustering.rst.txt
k) \leftarrow s(i, k) - max [ a(i, k') + s(i, k') \forall k'...Coefficient *s* for a single sample is then given as: .. math:: s = \frac{b...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
decomposition.rst.txt
If we use all the :math:`x_i`'s as columns to form a matrix :math:`\mathbf{X}`...:math:`\mathbf{X}` and all the :math:`h_i`'s as columns of a matrix :math:`\mathbf{H}`...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
install.rst.txt
prompt:: bash sudo pacman -S python-scikit-learn Debian/Ubuntu...scikit-learn.org/stable/_sources/install.rst.txt -
about.rst.txt
html#what-s-the-best-way-to-get-help-on-s...scikit-learn.org/stable/_sources/about.rst.txt -
neighbors.rst.txt
S.M., International Computer Science...Paper.pdf>`_, J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov,...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
ensemble.rst.txt
S.C. Bourassa, M. Hoesli, and D.F....print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(),...scikit-learn.org/stable/_sources/modules/ensemble.rst.txt