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check_increasing — scikit-learn 1.6.0 documenta...
y = [ 1 , 2 , 3 , 4 , 5 ], [ 2 , 4 , 6...scikit-learn.org/stable/modules/generated/sklearn.isotonic.check_increasing.html -
2. Unsupervised learning — scikit-learn 1.6.0 d...
1. Gaussian mixture models 2.1.1. Gaussian Mixture 2.1.2....Mixture 2.2. Manifold learning 2.2.1. Introduction 2.2.2. Isomap 2.2.3....scikit-learn.org/stable/unsupervised_learning.html -
smacof — scikit-learn 1.6.0 documentation
-1.07... ], [ 1.74..., -0.75...], [-1.79..., 1.83...]])...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.manifold.smacof.html -
Demonstrating the different strategies of KBins...
]]) centers_1 = np . array ([[ 0 , 0 ], [ 3 , 1 ]]) # construct...strategies ) + 1 , i ) ax . scatter ( X [:, 0 ], X [:, 1 ], edgecolors...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html -
RegressorChain — scikit-learn 1.6.0 documentation
= [[ 1 , 0 ], [ 0 , 1 ], [ 1 , 1 ]], [[ 0 , 2 ], [ 1 , 1 ], [...order = [ 0 , 1 , 2 , ... , Y . shape [ 1 ] - 1 ] The order of...scikit-learn.org/stable/modules/generated/sklearn.multioutput.RegressorChain.html -
MiniBatchNMF — scikit-learn 1.6.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Topic extraction...scikit-learn.org/stable/modules/generated/sklearn.decomposition.MiniBatchNMF.html -
RandomForestClassifier — scikit-learn 1.6.0 doc...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html -
TSNE — scikit-learn 1.6.0 documentation
1 , 1 ], [ 1 , 0 , 1 ], [ 1 , 1 , 1 ]]) >>> X_embedded...metric function. Added in version 1.1. init {“random”, “pca”} or ndarray...scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html -
Visualizing the stock market structure — scikit...
index ] = 1 dy = y - embedding [ 1 ] dy [ index ] = 1 this_dx =...alphas = np . logspace ( - 1.5 , 1 , num = 10 ) edge_model = covariance...scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html -
BayesianRidge — scikit-learn 1.6.0 documentation
[ 1 , 1 ], [ 2 , 2 ]], [ 0 , 1 , 2 ]) BayesianRidge()...>>> clf . predict ([[ 1 , 1 ]]) array([1.]) fit ( X , y , sample_weight...scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html