.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/20_basic/example_multioutput_regression.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_20_basic_example_multioutput_regression.py: ======================= Multi-output Regression ======================= The following example shows how to fit a multioutput regression model with *auto-sklearn*. .. GENERATED FROM PYTHON SOURCE LINES 10-20 .. code-block:: default import numpy as numpy from pprint import pprint from sklearn.datasets import make_regression from sklearn.metrics import r2_score from sklearn.model_selection import train_test_split from autosklearn.regression import AutoSklearnRegressor .. GENERATED FROM PYTHON SOURCE LINES 21-23 Data Loading ============ .. GENERATED FROM PYTHON SOURCE LINES 23-28 .. code-block:: default X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) .. GENERATED FROM PYTHON SOURCE LINES 29-31 Build and fit a regressor ========================= .. GENERATED FROM PYTHON SOURCE LINES 31-39 .. code-block:: default automl = AutoSklearnRegressor( time_left_for_this_task=120, per_run_time_limit=30, tmp_folder="/tmp/autosklearn_multioutput_regression_example_tmp", ) automl.fit(X_train, y_train, dataset_name="synthetic") .. rst-class:: sphx-glr-script-out .. code-block:: none AutoSklearnRegressor(ensemble_class=, per_run_time_limit=30, time_left_for_this_task=120, tmp_folder='/tmp/autosklearn_multioutput_regression_example_tmp') .. GENERATED FROM PYTHON SOURCE LINES 40-42 View the models found by auto-sklearn ===================================== .. GENERATED FROM PYTHON SOURCE LINES 42-46 .. code-block:: default print(automl.leaderboard()) .. rst-class:: sphx-glr-script-out .. code-block:: none rank ensemble_weight type cost duration model_id 17 1 0.88 gaussian_process 2.686994e-08 11.047750 4 2 0.12 gaussian_process 6.650299e-08 4.449338 .. GENERATED FROM PYTHON SOURCE LINES 47-49 Print the final ensemble constructed by auto-sklearn ==================================================== .. GENERATED FROM PYTHON SOURCE LINES 49-52 .. code-block:: default pprint(automl.show_models(), indent=4) .. rst-class:: sphx-glr-script-out .. code-block:: none { 4: { 'cost': 6.650299455568387e-08, 'data_preprocessor': , 'ensemble_weight': 0.12, 'feature_preprocessor': , 'model_id': 4, 'rank': 1, 'regressor': , 'sklearn_regressor': GaussianProcessRegressor(alpha=2.6231667524556984e-13, kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), n_restarts_optimizer=10, normalize_y=True, random_state=1)}, 17: { 'cost': 2.686994005074439e-08, 'data_preprocessor': , 'ensemble_weight': 0.88, 'feature_preprocessor': , 'model_id': 17, 'rank': 2, 'regressor': , 'sklearn_regressor': GaussianProcessRegressor(alpha=6.100934970841317e-14, kernel=RBF(length_scale=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), n_restarts_optimizer=10, normalize_y=True, random_state=1)}} .. GENERATED FROM PYTHON SOURCE LINES 53-55 Get the Score of the final ensemble =================================== .. GENERATED FROM PYTHON SOURCE LINES 55-59 .. code-block:: default predictions = automl.predict(X_test) print("R2 score:", r2_score(y_test, predictions)) .. rst-class:: sphx-glr-script-out .. code-block:: none R2 score: 0.9999999400500433 .. GENERATED FROM PYTHON SOURCE LINES 60-62 Get the configuration space =========================== .. GENERATED FROM PYTHON SOURCE LINES 62-65 .. code-block:: default # The configuration space is reduced, i.e. no SVM. print(automl.get_configuration_space(X_train, y_train)) .. rst-class:: sphx-glr-script-out .. code-block:: none Configuration space object: Hyperparameters: data_preprocessor:__choice__, Type: Categorical, Choices: {feature_type}, Default: feature_type data_preprocessor:feature_type:numerical_transformer:imputation:strategy, Type: Categorical, Choices: {mean, median, most_frequent}, Default: mean data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Type: Categorical, Choices: {minmax, none, normalize, power_transformer, quantile_transformer, robust_scaler, standardize}, Default: standardize data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles, Type: UniformInteger, Range: [10, 2000], Default: 1000 data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution, Type: Categorical, Choices: {normal, uniform}, Default: normal data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max, Type: UniformFloat, Range: [0.7, 0.999], Default: 0.75 data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min, Type: UniformFloat, Range: [0.001, 0.3], Default: 0.25 feature_preprocessor:__choice__, Type: Categorical, Choices: {extra_trees_preproc_for_regression, fast_ica, feature_agglomeration, kernel_pca, kitchen_sinks, no_preprocessing, nystroem_sampler, pca, polynomial, random_trees_embedding}, Default: no_preprocessing feature_preprocessor:extra_trees_preproc_for_regression:bootstrap, Type: Categorical, Choices: {True, False}, Default: False feature_preprocessor:extra_trees_preproc_for_regression:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse feature_preprocessor:extra_trees_preproc_for_regression:max_depth, Type: Constant, Value: None feature_preprocessor:extra_trees_preproc_for_regression:max_features, Type: UniformFloat, Range: [0.1, 1.0], Default: 1.0 feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes, Type: Constant, Value: None feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1 feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2 feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf, Type: Constant, Value: 0.0 feature_preprocessor:extra_trees_preproc_for_regression:n_estimators, Type: Constant, Value: 100 feature_preprocessor:fast_ica:algorithm, Type: Categorical, Choices: {parallel, deflation}, Default: parallel feature_preprocessor:fast_ica:fun, Type: Categorical, Choices: {logcosh, exp, cube}, Default: logcosh feature_preprocessor:fast_ica:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100 feature_preprocessor:fast_ica:whiten, Type: Categorical, Choices: {False, True}, Default: False feature_preprocessor:feature_agglomeration:affinity, Type: Categorical, Choices: {euclidean, manhattan, cosine}, Default: euclidean feature_preprocessor:feature_agglomeration:linkage, Type: Categorical, Choices: {ward, complete, average}, Default: ward feature_preprocessor:feature_agglomeration:n_clusters, Type: UniformInteger, Range: [2, 400], Default: 25 feature_preprocessor:feature_agglomeration:pooling_func, Type: Categorical, Choices: {mean, median, max}, Default: mean feature_preprocessor:kernel_pca:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0 feature_preprocessor:kernel_pca:degree, Type: UniformInteger, Range: [2, 5], Default: 3 feature_preprocessor:kernel_pca:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.01, on log-scale feature_preprocessor:kernel_pca:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf feature_preprocessor:kernel_pca:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100 feature_preprocessor:kitchen_sinks:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 1.0, on log-scale feature_preprocessor:kitchen_sinks:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale feature_preprocessor:nystroem_sampler:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0 feature_preprocessor:nystroem_sampler:degree, Type: UniformInteger, Range: [2, 5], Default: 3 feature_preprocessor:nystroem_sampler:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.1, on log-scale feature_preprocessor:nystroem_sampler:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf feature_preprocessor:nystroem_sampler:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale feature_preprocessor:pca:keep_variance, Type: UniformFloat, Range: [0.5, 0.9999], Default: 0.9999 feature_preprocessor:pca:whiten, Type: Categorical, Choices: {False, True}, Default: False feature_preprocessor:polynomial:degree, Type: UniformInteger, Range: [2, 3], Default: 2 feature_preprocessor:polynomial:include_bias, Type: Categorical, Choices: {True, False}, Default: True feature_preprocessor:polynomial:interaction_only, Type: Categorical, Choices: {False, True}, Default: False feature_preprocessor:random_trees_embedding:bootstrap, Type: Categorical, Choices: {True, False}, Default: True feature_preprocessor:random_trees_embedding:max_depth, Type: UniformInteger, Range: [2, 10], Default: 5 feature_preprocessor:random_trees_embedding:max_leaf_nodes, Type: Constant, Value: None feature_preprocessor:random_trees_embedding:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1 feature_preprocessor:random_trees_embedding:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2 feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf, Type: Constant, Value: 1.0 feature_preprocessor:random_trees_embedding:n_estimators, Type: UniformInteger, Range: [10, 100], Default: 10 regressor:__choice__, Type: Categorical, Choices: {decision_tree, extra_trees, gaussian_process, k_nearest_neighbors, random_forest}, Default: random_forest regressor:decision_tree:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse regressor:decision_tree:max_depth_factor, Type: UniformFloat, Range: [0.0, 2.0], Default: 0.5 regressor:decision_tree:max_features, Type: Constant, Value: 1.0 regressor:decision_tree:max_leaf_nodes, Type: Constant, Value: None regressor:decision_tree:min_impurity_decrease, Type: Constant, Value: 0.0 regressor:decision_tree:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1 regressor:decision_tree:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2 regressor:decision_tree:min_weight_fraction_leaf, Type: Constant, Value: 0.0 regressor:extra_trees:bootstrap, Type: Categorical, Choices: {True, False}, Default: False regressor:extra_trees:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse regressor:extra_trees:max_depth, Type: Constant, Value: None regressor:extra_trees:max_features, Type: UniformFloat, Range: [0.1, 1.0], Default: 1.0 regressor:extra_trees:max_leaf_nodes, Type: Constant, Value: None regressor:extra_trees:min_impurity_decrease, Type: Constant, Value: 0.0 regressor:extra_trees:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1 regressor:extra_trees:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2 regressor:extra_trees:min_weight_fraction_leaf, Type: Constant, Value: 0.0 regressor:gaussian_process:alpha, Type: UniformFloat, Range: [1e-14, 1.0], Default: 1e-08, on log-scale regressor:gaussian_process:thetaL, Type: UniformFloat, Range: [1e-10, 0.001], Default: 1e-06, on log-scale regressor:gaussian_process:thetaU, Type: UniformFloat, Range: [1.0, 100000.0], Default: 100000.0, on log-scale regressor:k_nearest_neighbors:n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 1, on log-scale regressor:k_nearest_neighbors:p, Type: Categorical, Choices: {1, 2}, Default: 2 regressor:k_nearest_neighbors:weights, Type: Categorical, Choices: {uniform, distance}, Default: uniform regressor:random_forest:bootstrap, Type: Categorical, Choices: {True, False}, Default: True regressor:random_forest:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse regressor:random_forest:max_depth, Type: Constant, Value: None regressor:random_forest:max_features, Type: UniformFloat, Range: [0.1, 1.0], Default: 1.0 regressor:random_forest:max_leaf_nodes, Type: Constant, Value: None regressor:random_forest:min_impurity_decrease, Type: Constant, Value: 0.0 regressor:random_forest:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1 regressor:random_forest:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2 regressor:random_forest:min_weight_fraction_leaf, Type: Constant, Value: 0.0 Conditions: data_preprocessor:feature_type:numerical_transformer:imputation:strategy | data_preprocessor:__choice__ == 'feature_type' data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ | data_preprocessor:__choice__ == 'feature_type' data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'quantile_transformer' data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'quantile_transformer' data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'robust_scaler' data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'robust_scaler' feature_preprocessor:extra_trees_preproc_for_regression:bootstrap | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:criterion | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:max_depth | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:max_features | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:extra_trees_preproc_for_regression:n_estimators | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression' feature_preprocessor:fast_ica:algorithm | feature_preprocessor:__choice__ == 'fast_ica' feature_preprocessor:fast_ica:fun | feature_preprocessor:__choice__ == 'fast_ica' feature_preprocessor:fast_ica:n_components | feature_preprocessor:fast_ica:whiten == 'True' feature_preprocessor:fast_ica:whiten | feature_preprocessor:__choice__ == 'fast_ica' feature_preprocessor:feature_agglomeration:affinity | feature_preprocessor:__choice__ == 'feature_agglomeration' feature_preprocessor:feature_agglomeration:linkage | feature_preprocessor:__choice__ == 'feature_agglomeration' feature_preprocessor:feature_agglomeration:n_clusters | feature_preprocessor:__choice__ == 'feature_agglomeration' feature_preprocessor:feature_agglomeration:pooling_func | feature_preprocessor:__choice__ == 'feature_agglomeration' feature_preprocessor:kernel_pca:coef0 | feature_preprocessor:kernel_pca:kernel in {'poly', 'sigmoid'} feature_preprocessor:kernel_pca:degree | feature_preprocessor:kernel_pca:kernel == 'poly' feature_preprocessor:kernel_pca:gamma | feature_preprocessor:kernel_pca:kernel in {'poly', 'rbf'} feature_preprocessor:kernel_pca:kernel | feature_preprocessor:__choice__ == 'kernel_pca' feature_preprocessor:kernel_pca:n_components | feature_preprocessor:__choice__ == 'kernel_pca' feature_preprocessor:kitchen_sinks:gamma | feature_preprocessor:__choice__ == 'kitchen_sinks' feature_preprocessor:kitchen_sinks:n_components | feature_preprocessor:__choice__ == 'kitchen_sinks' feature_preprocessor:nystroem_sampler:coef0 | feature_preprocessor:nystroem_sampler:kernel in {'poly', 'sigmoid'} feature_preprocessor:nystroem_sampler:degree | feature_preprocessor:nystroem_sampler:kernel == 'poly' feature_preprocessor:nystroem_sampler:gamma | feature_preprocessor:nystroem_sampler:kernel in {'poly', 'rbf', 'sigmoid'} feature_preprocessor:nystroem_sampler:kernel | feature_preprocessor:__choice__ == 'nystroem_sampler' feature_preprocessor:nystroem_sampler:n_components | feature_preprocessor:__choice__ == 'nystroem_sampler' feature_preprocessor:pca:keep_variance | feature_preprocessor:__choice__ == 'pca' feature_preprocessor:pca:whiten | feature_preprocessor:__choice__ == 'pca' feature_preprocessor:polynomial:degree | feature_preprocessor:__choice__ == 'polynomial' feature_preprocessor:polynomial:include_bias | feature_preprocessor:__choice__ == 'polynomial' feature_preprocessor:polynomial:interaction_only | feature_preprocessor:__choice__ == 'polynomial' feature_preprocessor:random_trees_embedding:bootstrap | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:max_depth | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:max_leaf_nodes | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:min_samples_leaf | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:min_samples_split | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf | feature_preprocessor:__choice__ == 'random_trees_embedding' feature_preprocessor:random_trees_embedding:n_estimators | feature_preprocessor:__choice__ == 'random_trees_embedding' regressor:decision_tree:criterion | regressor:__choice__ == 'decision_tree' regressor:decision_tree:max_depth_factor | regressor:__choice__ == 'decision_tree' regressor:decision_tree:max_features | regressor:__choice__ == 'decision_tree' regressor:decision_tree:max_leaf_nodes | regressor:__choice__ == 'decision_tree' regressor:decision_tree:min_impurity_decrease | regressor:__choice__ == 'decision_tree' regressor:decision_tree:min_samples_leaf | regressor:__choice__ == 'decision_tree' regressor:decision_tree:min_samples_split | regressor:__choice__ == 'decision_tree' regressor:decision_tree:min_weight_fraction_leaf | regressor:__choice__ == 'decision_tree' regressor:extra_trees:bootstrap | regressor:__choice__ == 'extra_trees' regressor:extra_trees:criterion | regressor:__choice__ == 'extra_trees' regressor:extra_trees:max_depth | regressor:__choice__ == 'extra_trees' regressor:extra_trees:max_features | regressor:__choice__ == 'extra_trees' regressor:extra_trees:max_leaf_nodes | regressor:__choice__ == 'extra_trees' regressor:extra_trees:min_impurity_decrease | regressor:__choice__ == 'extra_trees' regressor:extra_trees:min_samples_leaf | regressor:__choice__ == 'extra_trees' regressor:extra_trees:min_samples_split | regressor:__choice__ == 'extra_trees' regressor:extra_trees:min_weight_fraction_leaf | regressor:__choice__ == 'extra_trees' regressor:gaussian_process:alpha | regressor:__choice__ == 'gaussian_process' regressor:gaussian_process:thetaL | regressor:__choice__ == 'gaussian_process' regressor:gaussian_process:thetaU | regressor:__choice__ == 'gaussian_process' regressor:k_nearest_neighbors:n_neighbors | regressor:__choice__ == 'k_nearest_neighbors' regressor:k_nearest_neighbors:p | regressor:__choice__ == 'k_nearest_neighbors' regressor:k_nearest_neighbors:weights | regressor:__choice__ == 'k_nearest_neighbors' regressor:random_forest:bootstrap | regressor:__choice__ == 'random_forest' regressor:random_forest:criterion | regressor:__choice__ == 'random_forest' regressor:random_forest:max_depth | regressor:__choice__ == 'random_forest' regressor:random_forest:max_features | regressor:__choice__ == 'random_forest' regressor:random_forest:max_leaf_nodes | regressor:__choice__ == 'random_forest' regressor:random_forest:min_impurity_decrease | regressor:__choice__ == 'random_forest' regressor:random_forest:min_samples_leaf | regressor:__choice__ == 'random_forest' regressor:random_forest:min_samples_split | regressor:__choice__ == 'random_forest' regressor:random_forest:min_weight_fraction_leaf | regressor:__choice__ == 'random_forest' Forbidden Clauses: (Forbidden: feature_preprocessor:feature_agglomeration:affinity in {'cosine', 'manhattan'} && Forbidden: feature_preprocessor:feature_agglomeration:linkage == 'ward') (Forbidden: feature_preprocessor:__choice__ == 'random_trees_embedding' && Forbidden: regressor:__choice__ == 'gaussian_process') (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks') (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca') (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler') (Forbidden: regressor:__choice__ == 'extra_trees' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks') (Forbidden: regressor:__choice__ == 'extra_trees' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca') (Forbidden: regressor:__choice__ == 'extra_trees' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler') (Forbidden: regressor:__choice__ == 'gaussian_process' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks') (Forbidden: regressor:__choice__ == 'gaussian_process' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca') (Forbidden: regressor:__choice__ == 'gaussian_process' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler') (Forbidden: regressor:__choice__ == 'k_nearest_neighbors' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks') (Forbidden: regressor:__choice__ == 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https://mybinder.org/v2/gh/automl/auto-sklearn/master?urlpath=lab/tree/notebooks/examples/20_basic/example_multioutput_regression.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: example_multioutput_regression.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: example_multioutput_regression.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_