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Density Estimation for a Gaussian mixture — sci...
CS = plt . contour ( X , Y , Z , norm = LogNorm ( vmin = 1.0...CB = plt . colorbar ( CS , shrink = 0.8 , extend = "both" ) plt...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html -
RANSACRegressor — scikit-learn 1.7.2 documentation
estimator = None , * , min_samples = None , residual_threshold = None...is_data_valid = None , is_model_valid = None , max_trials = 100 , max_skips...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html -
Kernel Density Estimation — scikit-learn 1.7.2 ...
pca = PCA ( n_components = 15 , whiten = False ) data = pca ....new_data = kde . sample ( 44 , random_state = 0 ) new_data = pca ....scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
LeavePOut — scikit-learn 1.7.2 documentation
Train: index=[2 3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test:...Test: index=[0 2] Fold 2: Train: index=[1 2] Test: index=[0 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
Blind source separation using FastICA — scikit-...
n_samples = 2000 time = np . linspace ( 0 , 8 , n_samples ) s1 = np...( size = S . shape ) # Add noise S /= S . std ( axis = 0 ) # Standardize...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
Seasar(S2Container)の取扱い | DBFlute
<include path="convention.dicon"/> <include path="aop.dicon"/>...path="aop.dicon"/> <include path="dbflute.dicon"/> ... </components> SeasarのDicon構造...dbflute.seasar.org/ja/manual/reference/diway/seasar/index.html -
BaggingClassifier — scikit-learn 1.7.2 document...
special cases with k == 1 , otherwise k==n_classes . fit ( X ,...estimator = None , n_estimators = 10 , * , max_samples = 1.0 , max_features...scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html -
SelectFromModel — scikit-learn 1.7.2 documentation
threshold = None , prefit = False , norm_order = 1 , max_features...max_features = None , importance_getter = 'auto' ) [source] # Meta-transformer...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
ExtraTreeClassifier — scikit-learn 1.7.2 docume...
criterion = 'gini' , splitter = 'random' , max_depth = None , min_samples_split...min_weight_fraction_leaf = 0.0 , max_features = 'sqrt' , random_state = None ,...scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html -
3.1. Cross-validation: evaluating estimator per...
kernel = 'linear' , C = 1 , random_state = 0 ) >>> scores = cross_validate...(60,)) >>> clf = svm . SVC ( kernel = 'linear' , C = 1 ) . fit (...scikit-learn.org/stable/modules/cross_validation.html