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Comparison of LDA and PCA 2D projection of Iris...
[ y == i , 0 ], X_r2 [ y == i , 1 ], alpha = 0.8 , color = color...( X_r [ y == i , 0 ], X_r [ y == i , 1 ], color = color , alpha...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
Isomap — scikit-learn 1.7.0 documentation
eigen_solver = 'auto' , tol = 0 , max_iter = None , path_method = 'auto'...neighbors_algorithm = 'auto' , n_jobs = None , metric = 'minkowski' , p = 2 ,...scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html -
ExtraTreesRegressor — scikit-learn 1.7.0 docume...
n_estimators = 100 , * , criterion = 'squared_error' , max_depth = None...min_weight_fraction_leaf = 0.0 , max_features = 1.0 , max_leaf_nodes = None , ...scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html -
Release Highlights for scikit-learn 1.5 — sciki...
n_features = 100 , tail_strength = 0.1 , random_state = 0 ) pca = PCA...random_state = 0 ) pca = PCA ( n_components = 10 , svd_solver = "covariance_eigh"...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html -
lars_path — scikit-learn 1.7.0 documentation
alpha_min = 0 , method = 'lar' , copy_X = True , eps = np.float64...X , y , Xy = None , * , Gram = None , max_iter = 500 , alpha_min...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path.html -
Kernel PCA — scikit-learn 1.7.0 documentation
y = make_circles ( n_samples = 1_000 , factor = 0.3 , noise...test_ax ) = plt . subplots ( ncols = 2 , sharex = True , sharey...scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html -
LastaFlute移行 0.8.2 to 0.8.3 | LastaFlute
@Java // ========== // Verify Anything // ========== // ----------...transitionKey); } // ========== // Small Facade // ========== // ----------...dbflute.seasar.org/ja/lastaflute/howto/upgrade/migration/lamig082to083.html -
OOB Errors for Random Forests — scikit-learn 1....
y = make_classification ( n_samples = 500 , n_features = 25...n_clusters_per_class = 1 , n_informative = 15 , random_state = RANDOM_STATE...scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html -
LogisticRegressionCV — scikit-learn 1.7.0 docum...
Cs = 10 , fit_intercept = True , cv = None , dual = False...penalty = 'l2' , scoring = None , solver = 'lbfgs' , tol = 0.0001...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html -
Plot the support vectors in LinearSVC — scikit-...
clf = LinearSVC ( C = C , loss = "hinge" , random_state = 42 )...1 ], c = y , s = 30 , cmap = plt . cm . Paired ) ax = plt . gca...scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html