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Gaussian Mixture Model Sine Curve — sciki...
eigh ( covar ) v = 2.0 * np . sqrt ( 2.0 ) * np . sqrt ( v )...mean_precision_prior = 1e-2 , covariance_prior = 1e0 * np . eye ( 2 ), init_params...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html -
PoissonRegressor — scikit-learn 1.8.0 doc...
determination R^2. R^2 uses squared error and D^2 uses the deviance...() >>> X = [[ 1 , 2 ], [ 2 , 3 ], [ 3 , 4 ], [ 4 , 3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html -
mean_gamma_deviance — scikit-learn 1.8.0 ...
2. , 2. ] >>> mean_gamma_deviance...with the power parameter power=2 . It is invariant to scaling of...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_gamma_deviance.html -
Model Complexity Influence — scikit-learn...
"n_iter_no_change" : 2 , }, "changing_param"..."C" : 1e3 , "gamma" : 2 **- 15 }, "changing_param"...scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html -
make_classification — scikit-learn 1.8.0 ...
n_informative = 2 , n_redundant = 2 , n_repeated = 0 , n_classes = 2 , n_clusters_per_class...n_clusters_per_class = 2 , weights = None , flip_y = 0.01 , class_sep...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html -
kneighbors_graph — scikit-learn 1.8.0 doc...
p = 2 , metric_params = None , include_self...standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance...scikit-learn.org/stable/modules/generated/sklearn.neighbors.kneighbors_graph.html -
single_source_shortest_path_length — scik...
2: 2} >>> graph = np . ones...h ( graph , 2 ) . items ()) [(0, 1), (1, 1), (2, 0), (3, 1),...scikit-learn.org/stable/modules/generated/sklearn.utils.graph.single_source_shortest_path_length.... -
PLSRegression — scikit-learn 1.8.0 docume...
[ 2. , 2. , 2. ], [ 2. , 5. , 4. ]] >>>...y = [[ 0.1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ], [ 11.9 ,...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html -
7.9. Transforming the prediction target (y) ...
y = [[ 2 , 3 , 4 ], [ 2 ], [ 0 , 1 , 3 ], [ 0 , 1 , 2 , 3 , 4...>>> lb . fit ([ 1 , 2 , 6 , 4 , 2 ]) LabelBinarizer() >>>...scikit-learn.org/stable/modules/preprocessing_targets.html -
r2_score — scikit-learn 1.8.0 documentation
= [ - 2 , - 2 , - 2 ] >>> y_pred = [ - 2 , - 2 , - 2...= [ - 2 , - 2 , - 2 ] >>> y_pred = [ - 2 , - 2 , - 2...scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html