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Eddie Bergman: Update workflow files (#363)
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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

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‎development/_images/sphx_glr_example_plot_over_time_001.png

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‎development/_modules/autoPyTorch/api/tabular_classification.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/api/tabular_regression.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/components/base_choice.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/components/base_component.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/tabular_classification.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/tabular_regression.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/traditional_tabular_classification.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/autoPyTorch/pipeline/traditional_tabular_regression.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_modules/index.html

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&copy; Copyright 2014-2021, Machine Learning Professorship Freiburg.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.1.<br/>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 4.3.2.<br/>
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‎development/_sources/examples/20_basics/example_image_classification.rst.txt

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Pipeline Random Config:
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________________________________________
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Configuration:
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image_augmenter:GaussianBlur:sigma_min, Value: 1.2393224595131462
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image_augmenter:GaussianBlur:sigma_offset, Value: 2.748337224875126
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image_augmenter:GaussianBlur:use_augmenter, Value: True
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image_augmenter:GaussianNoise:use_augmenter, Value: False
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image_augmenter:RandomAffine:rotate, Value: 3
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image_augmenter:RandomAffine:scale_offset, Value: 0.061512456684185195
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image_augmenter:RandomAffine:shear, Value: 7
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.006995393031539399
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:sigma_offset, Value: 2.4639811066158748
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image_augmenter:GaussianNoise:use_augmenter, Value: True
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image_augmenter:RandomAffine:use_augmenter, Value: False
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image_augmenter:RandomCutout:use_augmenter, Value: False
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.18262170816649298
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image_augmenter:Resize:use_augmenter, Value: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.26355260384803597
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normalizer:__choice__, Value: 'NoNormalizer'
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.453 seconds)
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**Total running time of the script:** ( 0 minutes 5.321 seconds)
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.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

‎development/_sources/examples/20_basics/example_tabular_classification.rst.txt

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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7efcddd3f7f0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fa505fb6d30>
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.. code-block:: none
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{'accuracy': 0.838150289017341}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
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| 1 | None | KNNLearner | 0.16 |
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| 2 | None | CBLearner | 0.14 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 4 | SimpleImputer,OneHotEncoder,Normalizer,PowerTransformer | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 6 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 7 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 8 | None | SVMLearner | 0.04 |
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| 9 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | SimpleImputer,OneHotEncoder,MinMaxScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
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| 0 | None | CBLearner | 0.18 |
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| 1 | SimpleImputer,OneHotEncoder,Normalizer,KernelPCA | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 2 | SimpleImputer,OneHotEncoder,Normalizer,KitchenSink | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.14 |
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| 3 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 4 | SimpleImputer,NoEncoder,Normalizer,KitchenSink | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 5 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 6 | SimpleImputer,OneHotEncoder,Normalizer,PowerTransformer | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 7 | SimpleImputer,NoEncoder,Normalizer,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 8 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 9 | SimpleImputer,OneHotEncoder,MinMaxScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 10 | None | RFLearner | 0.02 |
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| 11 | None | ETLearner | 0.02 |
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| 12 | None | SVMLearner | 0.02 |
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| 13 | None | KNNLearner | 0.02 |
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| 14 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 15 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: Australian
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Optimisation Metric: accuracy
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Best validation score: 0.8713450292397661
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Number of target algorithm runs: 22
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Number of target algorithm runs: 30
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Number of successful target algorithm runs: 27
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Number of crashed target algorithm runs: 2
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Number of target algorithms that exceeded the time limit: 1
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**Total running time of the script:** ( 5 minutes 40.557 seconds)
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**Total running time of the script:** ( 5 minutes 25.275 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

‎development/_sources/examples/20_basics/example_tabular_regression.rst.txt

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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7efd726cf7f0>
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{'r2': 0.9445248186059718}
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| | Preprocessing | Estimator | Weight |
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|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.44 |
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| 1 | None | CBLearner | 0.42 |
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| 2 | None | LGBMLearner | 0.08 |
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| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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autoPyTorch results:
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Dataset name: 6d7729dd-5695-11ec-86a6-4f0b5f29c114
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Dataset name: af8b1267-6185-11ec-8760-a1d916d89faa
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Best validation score: 0.8644967965917701
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Number of target algorithm runs: 22
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Number of successful target algorithm runs: 20
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Number of crashed target algorithm runs: 1
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Best validation score: 0.8645385039886702
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Number of target algorithm runs: 34
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Number of successful target algorithm runs: 30
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Number of crashed target algorithm runs: 3
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**Total running time of the script:** ( 5 minutes 33.495 seconds)
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.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

‎development/_sources/examples/20_basics/sg_execution_times.rst.txt

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Computation times
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=================
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**11:30.242** total execution time for **examples_20_basics** files:
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**11:04.091** total execution time for **examples_20_basics** files:
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:41.232 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:33.495 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:40.557 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:25.275 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.453 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.321 | 0.0 MB |
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