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Ravin Kohli: [ADD] documentation for pipelines and steps (#329)
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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_002.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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@@ -86,24 +86,21 @@ Image Classification
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________________________________________
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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: 114
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image_augmenter:RandomAffine:scale_offset, Value: 0.11914307425363649
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image_augmenter:RandomAffine:shear, Value: 8
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image_augmenter:RandomAffine:translate_percent_offset, Value: 0.027196925844249223
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image_augmenter:RandomAffine:use_augmenter, Value: True
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image_augmenter:RandomCutout:p, Value: 0.24908507480752906
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image_augmenter:GaussianNoise:sigma_offset, Value: 2.9187627000740353
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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:p, Value: 0.9740940106250766
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image_augmenter:RandomCutout:use_augmenter, Value: True
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image_augmenter:Resize:use_augmenter, Value: False
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.2779993065586726
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normalizer:__choice__, Value: 'ImageNormalizer'
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image_augmenter:ZeroPadAndCrop:percent, Value: 0.3379048205724533
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normalizer:__choice__, Value: 'NoNormalizer'
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Fitting the pipeline...
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________________________________________
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ImageClassificationPipeline
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________________________________________
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0-) normalizer:
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ImageNormalizer
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NoNormalizer
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1-) preprocessing:
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EarlyPreprocessing
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 5.899 seconds)
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**Total running time of the script:** ( 0 minutes 8.728 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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@@ -133,7 +133,7 @@ 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 0x7f17d1cd15e0>
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<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fd84a7ed670>
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@@ -162,7 +162,7 @@ Print the final ensemble performance
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f17d1d96e50> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fd84a7edc40> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0011916160583496094, budget=0), TrajEntry(train_perf=0.1578947368421053, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0013194084167480469, budget=0), TrajEntry(train_perf=0.1578947368421053, incumbent_id=1, incumbent=Configuration:
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data_loader:batch_size, Value: 64
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encoder:__choice__, Value: 'OneHotEncoder'
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feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
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scaler:__choice__, Value: 'StandardScaler'
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trainer:StandardTrainer:weighted_loss, Value: True
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trainer:__choice__, Value: 'StandardTrainer'
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, ta_runs=1, ta_time_used=3.463505744934082, wallclock_time=4.493630886077881, budget=5.555555555555555), TrajEntry(train_perf=0.14035087719298245, incumbent_id=2, incumbent=Configuration:
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, ta_runs=1, ta_time_used=4.939751625061035, wallclock_time=5.972339868545532, budget=5.555555555555555), TrajEntry(train_perf=0.14035087719298245, incumbent_id=2, incumbent=Configuration:
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data_loader:batch_size, Value: 170
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encoder:__choice__, Value: 'NoEncoder'
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feature_preprocessor:Nystroem:kernel, Value: 'cosine'
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trainer:MixUpTrainer:alpha, Value: 0.758019642405335
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trainer:MixUpTrainer:weighted_loss, Value: False
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trainer:__choice__, Value: 'MixUpTrainer'
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, ta_runs=15, ta_time_used=109.08182263374329, wallclock_time=159.79244685173035, budget=50.0)]
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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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, ta_runs=15, ta_time_used=163.56785583496094, wallclock_time=220.1450080871582, budget=50.0)]
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{'accuracy': 0.8554913294797688}
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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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| 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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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 34.529 seconds)
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**Total running time of the script:** ( 5 minutes 25.958 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 0x7f1874c24ca0>
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<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fd8ed4f9ca0>
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.. code-block:: none
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<smac.runhistory.runhistory.RunHistory object at 0x7f185d369790> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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<smac.runhistory.runhistory.RunHistory object at 0x7fd8d5c37790> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0011248588562011719, budget=0), TrajEntry(train_perf=0.3195903909944855, incumbent_id=1, incumbent=Configuration:
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, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0010807514190673828, budget=0), TrajEntry(train_perf=0.30855378234329356, incumbent_id=1, incumbent=Configuration:
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, ta_runs=1, ta_time_used=2.1191773414611816, wallclock_time=3.1469626426696777, budget=5.555555555555555)]
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{'r2': 0.9445248186059718}
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, ta_runs=1, ta_time_used=3.0296387672424316, wallclock_time=4.0609352588653564, budget=5.555555555555555)]
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{'r2': 0.944631023189658}
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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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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 5 minutes 29.258 seconds)
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**Total running time of the script:** ( 5 minutes 44.149 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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**11:09.686** total execution time for **examples_20_basics** files:
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**11:18.834** total execution time for **examples_20_basics** files:
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+--------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:34.529 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:44.149 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``) | 05:29.258 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:25.958 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:05.899 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``) | 00:08.728 | 0.0 MB |
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