- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 1161 - 1170 of 3,496 for 1 (0.18 sec)
-
Faces recognition example using eigenfaces and ...
1 )[ - 1 ] true_name = target_names...y_test [ i ]] . rsplit ( " " , 1 )[ - 1 ] return "predicted: %s \n...scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html -
SGD: Maximum margin separating hyperplane — sci...
linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
pair_confusion_matrix — scikit-learn 1.6.0 docu...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) array([[8,...pair_confusion_matrix ([ 0 , 0 , 1 , 2 ], [ 0 , 0 , 1 , 1 ]) array([[8, 2],...scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html -
sklearn.decomposition — scikit-learn 1.7.dev0 d...
Matrix decomposition algorithms. These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be regarded as dimensionality reduction techniques. User guide. See the Decomposing...scikit-learn.org/dev/api/sklearn.decomposition.html -
Gaussian Mixture Models — scikit-learn 1.6.0 do...
Examples concerning the sklearn.mixture module. Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture Density Estimation for a Gaussian mixture GMM Initialization Methods GMM cov...scikit-learn.org/stable/auto_examples/mixture/index.html -
check_X_y — scikit-learn 1.7.dev0 documentation
ensure_min_samples = 1 , ensure_min_features = 1 , y_numeric = False...>>> X = [[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]] >>> y = [ 1 , 2 , 3 ]...scikit-learn.org/dev/modules/generated/sklearn.utils.check_X_y.html -
__sklearn_is_fitted__ as Developer API — scikit...
scikit-learn 1.6 Release Highlights for scikit-learn 1.6 Metadata...__init__ ( self , parameter = 1 ): self . parameter = parameter...scikit-learn.org/stable/auto_examples/developing_estimators/sklearn_is_fitted.html -
Blind source separation using FastICA — scikit-...
array ([[ 1 , 1 , 1 ], [ 0.5 , 2 , 1.0 ], [ 1.5 , 1.0 , 2.0 ]])...models , names ), 1 ): plt . subplot ( 4 , 1 , ii ) plt . title...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
nan_euclidean_distances — scikit-learn 1.6.0 do...
6] and [1, na, 4, 5] is: \[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]...float ( "NaN" ) >>> X = [[ 0 , 1 ], [ 1 , nan ]] >>> nan_euclidean_distances...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html -
paired_cosine_distances — scikit-learn 1.7.dev0...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html