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load_iris — scikit-learn 1.6.0 documentation
scikit-learn 1.2 Release Highlights for scikit-learn 1.2 Release...10 , 25 , 50 ]] array([0, 0, 1]) >>> list ( data . target_names...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html -
load_sample_image — scikit-learn 1.6.0 document...
Skip to main content Back to top Ctrl + K GitHub Choose version load_sample_image # sklearn.datasets. load_sample_ima...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_sample_image.html -
sklearn.naive_bayes — scikit-learn 1.6.0 docume...
Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...scikit-learn.org/stable/api/sklearn.naive_bayes.html -
クラス階層 (DBFlute Maven Plugin 1.0.0 API)
概要 パッケージ クラス 使用 階層ツリー 非推奨 索引 ヘルプ 前 次 フレーム フレームなし すべてのクラス すべてのパッケージの階層 パッケージ階層: org.seasar.dbflute.maven.plugin , org....dbflute.seasar.org/maven/plugin/apidocs/overview-tree.html -
Examples based on real world datasets — scikit-...
Applications to real world problems with some medium sized datasets or interactive user interface. Compressive sensing: tomography reconstruction with L1 prior (Lasso) Faces recognition example usi...scikit-learn.org/stable/auto_examples/applications/index.html -
register_parallel_backend — scikit-learn 1.6.0 ...
Skip to main content Back to top Ctrl + K GitHub Choose version register_parallel_backend # sklearn.utils. register_p...scikit-learn.org/stable/modules/generated/sklearn.utils.register_parallel_backend.html -
sklearn.random_projection — scikit-learn 1.6.0 ...
Random projection transformers. Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional ...scikit-learn.org/stable/api/sklearn.random_projection.html -
sklearn.kernel_approximation — scikit-learn 1.6...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html -
Plot classification boundaries with different S...
1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ]) # Plotting settings...], [ - 1.5 , - 1.0 ], [ - 1.4 , - 0.9 ], [ - 1.3 , - 1.2 ], [...scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html -
6.7. Kernel Approximation — scikit-learn 1.6.0 ...
[ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...\Lambda^{-1} U_1^T K_{21}^T \\&= K_{21} U_1 \Lambda^{-1} U_1^T K_{21}^T...scikit-learn.org/stable/modules/kernel_approximation.html