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adjusted_rand_score — scikit-learn 1.8.0 docume...
2 ], [ 0 , 0 , 1 , 1 ]) 0.57 ARI..., 0 , 1 , 1 ], [ 0 , 0 , 1 , 2 ]) 0.57 If classes members are...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
Multiclass sparse logistic regression on 20newg...
solver=saga] Number of epochs: 2 [model=One versus Rest, solver=saga]...solver=saga] Number of epochs: 2 [model=Multinomial, solver=saga]...scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html -
plot_hgbt_regression.rst.txt
2. :ref:`categorical_support_gbdt`,...showcasing all points except 2 and 6 in a real life setting....scikit-learn.org/stable/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt -
plot_release_highlights_1_8_0.ipynb
2, 2)\nx2, y2 = S_scaling.T\nax2.scatter(x2,...complexity compared to\n`O(n**2)` previously, which allows to...scikit-learn.org/stable/_downloads/826f1a05a1b6ad38fb98368f39e30aa6/plot_release_highlights_1_8_0... -
OutlierMixin — scikit-learn 1.8.0 documentation
2 ], [ 2 , 3 ], [ 3 , 4 ]]) >>> estimator...scikit-learn.org/stable/modules/generated/sklearn.base.OutlierMixin.html -
GaussianNB — scikit-learn 1.8.0 documentation
2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2 ]) >>>...- 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1 ], [ 3...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
test.rst
第一項目 - Lorem ipsum 2. 第二項目 - 吾輩は猫である 3. 第三項目 - Test...dolor sit amet | +----+---- | 2 | 吾輩は猫である | 夏目漱石の小説 | +----+----...raw.githubusercontent.com/codelibs/fess-testdata/master/files/markdown/test.rst -
RandomizedSearchCV — scikit-learn 1.8.0 documen...
2 0.84 … 3 ‘rbf’ 0.3 0.70 … 2 will be represented...verbose = 0 , pre_dispatch = '2*n_jobs' , random_state = None...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html -
Decision Boundaries of Multinomial and One-vs-R...
2 ], [ - 0.4 , 1.2 ]] X = np . dot ( X ,..."Feature 1" , ylabel = "Feature 2" ) _ = ax . legend ( * scatter...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html -
CountVectorizer — scikit-learn 1.8.0 documentation
2) means unigrams and bigrams, and (2, 2) means only...= 'word' , ngram_range = ( 2 , 2 )) >>> X2 = vectorizer2 . fit_transform...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html