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silhouette_score — scikit-learn 1.7.2 documenta...
The best value is 1 and the worst value is -1. Values near 0 indicate...2 <= n_labels <= n_samples - 1 . This function returns the mean...scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html -
Bunch — scikit-learn 1.7.2 documentation
import Bunch >>> b = Bunch ( a = 1 , b = 2 ) >>> b [ 'b' ] 2 >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
KernelDensity — scikit-learn 1.7.2 documentation
log_density array([-1.52955942, -1.51462041, -1.60244657]) fit (...KernelDensity ( * , bandwidth = 1.0 , algorithm = 'auto' , kernel...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html -
completeness_score — scikit-learn 1.7.2 documen...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
precision_score — scikit-learn 1.7.2 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred = [[...[[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>> precision_score...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html -
RBF — scikit-learn 1.7.2 documentation
return_X_y = True ) >>> kernel = 1.0 * RBF ( 1.0 ) >>> gpc = GaussianProcessClass...ernels. RBF ( length_scale = 1.0 , length_scale_bounds = (1e-05,...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html -
f1_score — scikit-learn 1.7.2 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred = [[...[[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>> f1_score ( y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
confusion_matrix — scikit-learn 1.7.2 documenta...
1 , 0 , 1 ], [ 1 , 1 , 1 , 0 ]) . ravel ()...negatives is \(C_{1,0}\) , true positives is \(C_{1,1}\) and false...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
minmax_scale — scikit-learn 1.7.2 documentation
1 , 2 ], [ - 1 , 0 , 1 ]] >>> minmax_scale...independently array([[0., 1., 1.], [1., 0., 0.]]) >>> minmax_scale...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
GaussianProcessRegressor — scikit-learn 1.7.2 d...
ConstantKernel(1.0, constant_value_bounds="fixed") * RBF(1.0, lengt...implementation is based on Algorithm 2.1 of [RW2006] . In addition to standard...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html