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Universal Profiling - continuous profiling that...
Learn more Synthetic monitoring Test user journeys to deliver a great...www.elastic.co/observability/universal-profiling -
make_regression — scikit-learn 1.5.0 documentation
Train error vs Test error Train error vs Test error Effect of...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html -
DecisionTreeClassifier — scikit-learn 1.5.0 doc...
splitter {“best”, “random”}, default=”best” The strategy used...Supported strategies are “best” to choose the best split and “random”...scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html -
Elastic Generative AI Tools and Capabilities | ...
Test drive serverless Generative...meaning and context — across text, images, videos, audio, geo-location,...www.elastic.co/generative-ai -
NBC News special features and projects | NBC News
Test your knowledge of Olympic sports...every state Specials See our best illustrations and photographs...www.nbcnews.com/specials -
VotingClassifier — scikit-learn 1.5.0 documenta...
the mean accuracy on the given test data and labels. In multi-label...shape (n_samples, n_features) Test samples. y array-like of shape...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html -
ExtraTreesRegressor — scikit-learn 1.5.0 docume...
X_test , y_train , y_test = train_test_split ( ......y_train ) >>> reg . score ( X_test , y_test ) 0.2727... apply ( X )...scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html -
load_iris — scikit-learn 1.5.0 documentation
(ROC) Nested versus non-nested cross-validation Nested versus...(ROC) with cross validation Test with permutations the significance...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html -
PassiveAggressiveClassifier — scikit-learn 1.5....
classification of text documents Out-of-core classification of text documents...the mean accuracy on the given test data and labels. In multi-label...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html -
MLPClassifier — scikit-learn 1.5.0 documentation
X_test , y_train , y_test = train_test_split ( X ,...1]) >>> clf . score ( X_test , y_test ) 0.8... fit ( X , y ) [source]...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html