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ledoit_wolf — scikit-learn 1.5.2 documentation
regularized (shrunk) covariance is: (1 - shrinkage) * cov + shrinkage...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf.html -
Normal, Ledoit-Wolf and OAS Linear Discriminant...
n_features_range = range ( 1 , n_features_max + 1 , step ) for n_features...n_samples = n_samples , n_features = 1 , centers = [[ - 2 ], [ 2 ]])...scikit-learn.org/stable/auto_examples/classification/plot_lda.html -
load_wine — scikit-learn 1.5.2 documentation
1, 2]) >>> list ( data . target_names...[np.str_('class_0'), np.str_('class_1'), np.str_('class_2')] Gallery...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html -
f_regression — scikit-learn 1.5.2 documentation
r_regression values lie in [-1, 1] and can thus be negative. f_regression...set to 0.0 . Added in version 1.1. Returns : f_statistic ndarray...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html -
Post-hoc tuning the cut-off point of decision f...
boxplot ( ax = ax [ 1 ]) ax [ 1 ] . set_title ( "Tuned model"...scikit-learn 1.5 Release Highlights for scikit-learn 1.5 Post-tuning...scikit-learn.org/stable/auto_examples/model_selection/plot_tuned_decision_threshold.html -
Bisecting K-Means and Regular K-Means Performan...
1 ], s = 10 , c = algo . labels_...centers [:, 0 ], centers [:, 1 ], c = "r" , s = 20 ) axs [ j...scikit-learn.org/stable/auto_examples/cluster/plot_bisect_kmeans.html -
移行 1.0.2 to 1.0.3 | DBFlute
SiteMap | Author's Blog 移行 1.0.2 to 1.0.3 お約束の注意点 環境上の注意点 Inne...dbflute.seasar.org/ja/environment/upgrade/migration/migrate102to103.html -
移行 1.0.3 to 1.0.4A | DBFlute
SiteMap | Author's Blog 移行 1.0.3 to 1.0.4A お約束の注意点 環境上の注意点 Rep...dbflute.seasar.org/ja/environment/upgrade/migration/migrate103to104A.html -
移行 1.0.4H to 1.0.4J | DBFlute
SiteMap | Author's Blog 移行 1.0.4H to 1.0.4J お約束の注意点 環境上の注意点 ソースコードのデフォルト改行コードをLFに...dbflute.seasar.org/ja/environment/upgrade/migration/migrate104Hto104J.html -
orthogonal_mp — scikit-learn 1.5.2 documentation
) >>> X [: 1 ,] @ coef array([-78.68...]) On...scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp.html