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Ravin Kohli: change worst possible result of r2 (#340)
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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_visualization_001.png

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‎development/_images/sphx_glr_example_visualization_thumb.png

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

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@@ -87,15 +87,15 @@ Image Classification
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Configuration:
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image_augmenter:GaussianBlur:use_augmenter, Value: False
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image_augmenter:GaussianNoise:use_augmenter, Value: False
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image_augmenter:RandomAffine:rotate, Value: 239
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image_augmenter:RandomAffine:scale_offset, Value: 0.056925342345860934
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image_augmenter:RandomAffine:shear, Value: 40
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.32451436908855363
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image_augmenter:RandomAffine:rotate, Value: 339
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image_augmenter:RandomAffine:scale_offset, Value: 0.3007791010350451
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image_augmenter:RandomAffine:shear, Value: 26
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.03882945703151322
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:RandomCutout:p, Value: 0.5746859534468014
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image_augmenter:RandomCutout:p, Value: 0.2663596033164267
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: True
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.033334448480574985
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.39730616568269495
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normalizer:__choice__, Value: 'NoNormalizer'
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Fitting the pipeline...
@@ -175,7 +175,7 @@ Image Classification
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.526 seconds)
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**Total running time of the script:** ( 0 minutes 8.173 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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@@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f90b38a3220>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f81756dd610>
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@@ -165,23 +165,20 @@ Print the final ensemble performance
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.. code-block:: none
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{'accuracy': 0.8554913294797688}
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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 | 0.18 |
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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 | SVMLearner | 0.12 |
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| 3 | None | CBLearner | 0.1 |
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| 4 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 5 | SimpleImputer,OneHotEncoder,Normalizer,PowerTransformer | embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 6 | None | RFLearner | 0.06 |
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| 7 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 8 | SimpleImputer,OneHotEncoder,MinMaxScaler,PowerTransformer | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 9 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
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| 11 | SimpleImputer,OneHotEncoder,Normalizer,TruncSVD | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 12 | SimpleImputer,OneHotEncoder,MinMaxScaler,TruncSVD | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 13 | None | ETLearner | 0.02 |
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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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autoPyTorch results:
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Dataset name: Australian
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Optimisation Metric: accuracy
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 35.166 seconds)
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**Total running time of the script:** ( 5 minutes 35.112 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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@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
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.. code-block:: none
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f9155e95ca0>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f8217ed1ca0>
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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: 70b3044c-4b9e-11ec-870d-13e6b1aae6bc
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Dataset name: e0ac8c9c-4bbd-11ec-870e-7746ea156edb
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Optimisation Metric: r2
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Best validation score: 0.8644967965917701
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Number of target algorithm runs: 23
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Number of successful target algorithm runs: 18
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Number of crashed target algorithm runs: 3
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Number of target algorithm runs: 24
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Number of successful target algorithm runs: 20
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Number of crashed target algorithm runs: 2
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Number of target algorithms that exceeded the time limit: 2
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Number of target algorithms that exceeded the memory limit: 0
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@@ -183,7 +183,7 @@ Print the final ensemble performance
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 36.742 seconds)
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**Total running time of the script:** ( 5 minutes 34.172 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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**11:20.434** total execution time for **examples_20_basics** files:
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**11:17.457** total execution time for **examples_20_basics** files:
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:36.742 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:35.112 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:35.166 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:34.172 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.526 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.173 | 0.0 MB |
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+

‎development/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt

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@@ -57,7 +57,7 @@ the search. Currently, there are two changes that can be made to the space:-
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| 5 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 6 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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autoPyTorch results:
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Dataset name: c6d94f6c-4ba2-11ec-870d-13e6b1aae6bc
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Dataset name: 4064d3b1-4bc2-11ec-870e-7746ea156edb
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Optimisation Metric: accuracy
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Best validation score: 0.8596491228070176
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Number of target algorithm runs: 17
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Number of target algorithms that exceeded the time limit: 1
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Number of target algorithms that exceeded the memory limit: 0
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{'accuracy': 0.8728323699421965}
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{'accuracy': 0.861271676300578}
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| | Preprocessing | Estimator | Weight |
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|---:|:--------------------------------------------------------------|:----------------------------------------------------------------|---------:|
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| 0 | None | RFLearner | 0.34 |
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| 1 | None | ETLearner | 0.26 |
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| 2 | None | LGBMLearner | 0.2 |
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| 3 | None | KNNLearner | 0.08 |
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| 4 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
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| 5 | None | SVMLearner | 0.04 |
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| 6 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
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| 0 | SimpleImputer,NoEncoder,Normalizer,PowerTransformer | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.26 |
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| 1 | None | KNNLearner | 0.14 |
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| 2 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.12 |
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| 3 | None | RFLearner | 0.1 |
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| 4 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
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| 5 | None | ETLearner | 0.08 |
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| 6 | None | SVMLearner | 0.08 |
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| 7 | SimpleImputer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.08 |
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| 8 | None | LGBMLearner | 0.04 |
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autoPyTorch results:
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Dataset name: 306ecee6-4ba3-11ec-870d-13e6b1aae6bc
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Dataset name: ad6f4912-4bc2-11ec-870e-7746ea156edb
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Optimisation Metric: accuracy
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Best validation score: 0.8596491228070176
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Number of target algorithm runs: 20
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Number of successful target algorithm runs: 13
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Number of successful target algorithm runs: 14
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Number of crashed target algorithm runs: 5
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Number of target algorithms that exceeded the time limit: 1
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Number of target algorithms that exceeded the memory limit: 1
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Number of target algorithms that exceeded the memory limit: 0
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 55.214 seconds)
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**Total running time of the script:** ( 5 minutes 59.297 seconds)
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.. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

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