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trustworthiness sklearn.manifold.trustworthiness(X, X_embedded, *, n_neighbors=5, metric='euclidean') trustworthiness 파라미터 Xndarray of shape (n_samples, n_features) or (n_samples, n_samples) X_embeddedndarray of shape (n_samples, n_components) n_neighborsint, default=5 metricstr or callable, default=’euclidean’

spectral_embedding sklearn.manifold.spectral_embedding(adjacency, *, n_components=8, eigen_solver=None, random_state=None, eigen_tol=0.0, norm_laplacian=True, drop_first=True) spectral_embedding 파라미터 adjacency{array-like, sparse graph} of shape (n_samples, n_samples) n_componentsint, default=8 eigen_solver{‘arpack’, ‘lobpcg’, ‘amg’}, default=None random_stateint, RandomState instance or None, de..

smacof 파라미터 sklearn.manifold.smacof(dissimilarities, *, metric=True, n_components=2, init=None, n_init=8, n_jobs=None, max_iter=300, verbose=0, eps=0.001, random_state=None, return_n_iter=False) smacof 파라미터 dissimilaritiesndarray of shape (n_samples, n_samples) metricbool, default=True n_componentsint, default=2 initndarray of shape (n_samples, n_components), default=None n_initint, default=8 n_..

locally_linear_embedding sklearn.manifold.locally_linear_embedding(X, *, n_neighbors, n_components, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, random_state=None, n_jobs=None) locally_linear_embedding 파라미터 X{array-like, NearestNeighbors} n_neighborsint n_componentsint regfloat, default=1e-3 eigen_solver{‘auto’, ‘arpack’, ‘de..

TSNE class sklearn.manifold.TSNE(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate='warn', n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', init='warn', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None, square_distances='legacy') TSNE 파라미터 n_componentsint, default=2 perplexityfloat, default=30.0 early_exaggeratio..

SpectralEmbedding class sklearn.manifold.SpectralEmbedding(n_components=2, *, affinity='nearest_neighbors', gamma=None, random_state=None, eigen_solver=None, n_neighbors=None, n_jobs=None) SpectralEmbedding 파라미터 n_componentsint, 기본값=2 affinity{‘nearest_neighbors’, ‘rbf’, ‘precomputed’, ‘precomputed_nearest_neighbors’} or callable, 기본값=’nearest_neighbors’ gammafloat, 기본값=None random_stateint, Ran..

MDS class sklearn.manifold.MDS(n_components=2, *, metric=True, n_init=4, max_iter=300, verbose=0, eps=0.001, n_jobs=None, random_state=None, dissimilarity='euclidean') MDS 파라미터 n_componentsint, 기본값=2 metricbool, 기본값=True n_initint, 기본값=4 max_iterint, 기본값=300 verboseint, 기본값=0 epsfloat, 기본값=1e-3 n_jobsint, 기본값=None random_stateint, RandomState instance or None, 기본값=None dissimilarity{‘euclidean’,..

LocallyLinearEmbedding class sklearn.manifold.LocallyLinearEmbedding(*, n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=None) LocallyLinearEmbedding 파라미터 n_neighborsint, 기본값=5 각 점에 대해 고려할 이웃의 수입니다. n_componentsint, 기본값=2 매니폴드의 좌표 수입니다. regfloat,..

sklearn.manifold.Isomap 파라미터 class sklearn.manifold.Isomap(*, n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=None, metric='minkowski', p=2, metric_params=None) sklearn.manifold.Isomap 파라미터 n_neighborsint, 기본값=5 각 점에 대해 고려할 이웃의 수입니다. n_componentsint, 기본값=2 매니폴드의 좌표 수입니다. eigen_solver{‘auto’, ‘arpack’, ‘dense’}, 기본값=..

ridge_regression sklearn.linear_model.ridge_regression(X, y, alpha, *, sample_weight=None, solver='auto', max_iter=None, tol=0.001, verbose=0, positive=False, random_state=None, return_n_iter=False, return_intercept=False, check_input=True) ridge_regression 파라미터 X{ndarray, sparse matrix, LinearOperator} of shape (n_samples, n_features) yndarray of shape (n_samples,) or (n_samples, n_targets) alp..