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get_scorer — scikit-learn 1.7.2 documenta...
2 ], ( - 1 , 1 )) >>> y...scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer.html -
Gaussian processes on discrete data structures ...
2.0 , 2.0 , 3.0 , 3.0 ]) training_idx...Y [ training_idx ], width = 0.2 , color = "r" , alpha...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html -
Curve Fitting with Bayesian Ridge Regression &#...
sin ( 2 * np . pi * x ) size = 25 rng..., axes = plt . subplots ( 1 , 2 , figsize = ( 8 , 4 )) for i ,...scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html -
Effect of varying threshold for self-training &...
capsize = 2 , color = "b" ) ax1.... std ( axis = 1 ), capsize = 2 , color = "g" , ) ax2...scikit-learn.org/stable/auto_examples/semi_supervised/plot_self_training_varying_threshold.html -
Detection error tradeoff (DET) curve — sc...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state...ax_det ] = plt . subplots ( 1 , 2 , figsize = ( 11 , 5 )) ax_roc...scikit-learn.org/stable/auto_examples/model_selection/plot_det.html -
Categorical Feature Support in Gradient Boostin...
2, etc., and treated as continuous...ax2 ) = plt . subplots ( 1 , 2 , figsize = ( 12 , 8 )) plot_info...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_categorical.html -
MaxAbsScaler — scikit-learn 1.7.2 documen...
2. ], ... [ 2. , 0. , 0. ], ... [ 0. ,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html -
Kernel Density Estimate of Species Distribution...
for i in range ( 2 ): plt . subplot ( 1 , 2 , i + 1 ) # construct...scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html -
partial_dependence — scikit-learn 1.7.2 d...
2. feature_names array-like of shape...dataframe. Added in version 1.2. response_method {‘auto’, ‘predict_proba’,...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
13. Choosing the right estimator — scikit...
Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problem...scikit-learn.org/stable/machine_learning_map.html