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check_random_state — scikit-learn 1.7.1 documen...
check_random_state ( 42 ) RandomState(MT19937) at 0x... Gallery examples...sklearn.utils. check_random_state ( seed ) [source] # Turn seed into...scikit-learn.org/stable/modules/generated/sklearn.utils.check_random_state.html -
SimpleImputer — scikit-learn 1.7.1 documentation
transform ( X )) [[ 7. 2. 3. ] [ 4. 3.5 6. ] [10. 3.5 9. ]] For...imp_mean . fit ([[ 7 , 2 , 3 ], [ 4 , np . nan , 6 ], [ 10 , 5 , 9...scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html -
SelectFromModel — scikit-learn 1.7.1 documentation
- 1.34 , 0.31 ], ... [ - 2.79 , - 0.02 , - 0.85 ], ... [ - 1.34...1.34 , - 0.48 , - 2.55 ], ... [ 1.92 , 1.48 , 0.65 ]] >>> y =...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
3.3. Tuning the decision threshold for class pr...
, 0.06 ], [0.0416, 0.9583], [0.0416, 0.9583]]) >>> classifier...make_classification ( ... n_samples = 1_000 , weights = [ 0.1 , 0.9 ], random_state...scikit-learn.org/stable/modules/classification_threshold.html -
9.1. Strategies to scale computationally: bigge...
algorithm 9.1.1.1. Streaming instances # Basically, 1. may be a...documents. 9.1.1.3. Incremental learning # Finally, for 3. we have...scikit-learn.org/stable/computing/scaling_strategies.html -
check_is_fitted — scikit-learn 1.7.1 documentation
fitted yet. >>> lr . fit ([[ 1 , 2 ], [ 1 , 3 ]], [ 1 , 0 ]) LogisticRegression()...["coef_", "estimator_", ...], "coef_" If None , estimator is considered...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html -
Pipeline — scikit-learn 1.7.1 documentation
'scaler' , StandardScaler ()), ( 'svc' , SVC ())]) >>> # The...train_test_split ( X , y , ... random_state = 0 ) >>> pipe = Pipeline ([( 'scaler'...scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html -
ClassifierChain — scikit-learn 1.7.1 documentation
array([[1., 1., 0.], [1., 0., 0.], [0., 1., 0.]]) >>> chain . predict_proba...matrix Y.: order = [ 0 , 1 , 2 , ... , Y . shape [ 1 ] - 1 ] The...scikit-learn.org/stable/modules/generated/sklearn.multioutput.ClassifierChain.html -
load_iris — scikit-learn 1.7.1 documentation
data . target [[ 10 , 25 , 50 ]] array([0, 0, 1]) >>> list ( data...data . target_names ) [np.str_('setosa'), np.str_('versicolor'),...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html -
GradientBoostingRegressor — scikit-learn 1.7.1 ...
{‘squared_error’, ‘absolute_error’, ‘huber’, ‘quantile’}, default=’squared_error’...= None , tol = 0.0001 , ccp_alpha = 0.0 ) [source] # Gradient...scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html