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‎articles/machine-learning/algorithm-module-reference/designer-error-codes.md

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Resolution:
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If you include a label column in the column selection but it isn’t recognized, use the [Edit Metadata](edit-metadata.md) module to mark it as a label column.
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If you include a label column in the column selection but it isn't recognized, use the [Edit Metadata](edit-metadata.md) module to mark it as a label column.
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<!--Use the [Summarize Data](summarize-data.md) module to generate a report that shows how many values are missing in each column. -->Then, you can use the [Clean Missing Data](clean-missing-data.md) module to remove rows with missing values in the label column.
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<!--Use the [Summarize Data](summarize-data.md) module to generate a report that shows how many values are missing in each column. -->
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Then, you can use the [Clean Missing Data](clean-missing-data.md) module to remove rows with missing values in the label column.
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Check your input datasets to make sure that they contain valid data, and enough rows to satisfy the requirements of the operation. Many algorithms will generate an error message if they require some minimum number rows of data, but the data contains only a few rows, or only a header.
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‎articles/machine-learning/algorithm-module-reference/execute-python-script.md

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- Anaconda 4.5+ distribution for Python 3.6
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The pre-installed packages are:
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- asn1crypto==0.24.0
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- attrs==19.1.0
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- azure-common==1.1.18
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- azure-storage-blob==1.5.0
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- azure-storage-common==1.4.0
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- certifi==2019.3.9
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- cffi==1.12.2
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- chardet==3.0.4
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- cryptography==2.6.1
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- distro==1.4.0
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- idna==2.8
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- jsonschema==3.0.1
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- lightgbm==2.2.3
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- more-itertools==6.0.0
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- numpy==1.16.2
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- pandas==0.24.2
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- Pillow==6.0.0
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- pip==19.0.3
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- pyarrow==0.12.1
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- pycparser==2.19
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- pycryptodomex==3.7.3
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- pyrsistent==0.14.11
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- python-dateutil==2.8.0
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- pytz==2018.9
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- requests==2.21.0
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- scikit-learn==0.20.3
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- scipy==1.2.1
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- setuptools==40.8.0
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- six==1.12.0
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- torch==1.0.1.post2
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- torchvision==0.2.2.post3
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- urllib3==1.24.1
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- wheel==0.33.1
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- adal==1.2.2
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- applicationinsights==0.11.9
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- attrs==19.3.0
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- azure-common==1.1.25
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- azure-core==1.3.0
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- azure-graphrbac==0.61.1
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- azure-identity==1.3.0
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- azure-mgmt-authorization==0.60.0
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- azure-mgmt-containerregistry==2.8.0
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- azure-mgmt-keyvault==2.2.0
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- azure-mgmt-resource==8.0.1
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- azure-mgmt-storage==8.0.0
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- azure-storage-blob==1.5.0
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- azure-storage-common==1.4.2
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- azureml-core==1.1.5.5
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- azureml-dataprep-native==14.1.0
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- azureml-dataprep==1.3.5
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- azureml-defaults==1.1.5.1
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- azureml-designer-classic-modules==0.0.118
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- azureml-designer-core==0.0.31
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- azureml-designer-internal==0.0.18
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- azureml-model-management-sdk==1.0.1b6.post1
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- azureml-pipeline-core==1.1.5
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- azureml-telemetry==1.1.5.3
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- backports.tempfile==1.0
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- backports.weakref==1.0.post1
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- boto3==1.12.29
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- botocore==1.15.29
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- cachetools==4.0.0
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- certifi==2019.11.28
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- cffi==1.12.3
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- chardet==3.0.4
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- click==7.1.1
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- cloudpickle==1.3.0
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- configparser==3.7.4
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- contextlib2==0.6.0.post1
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- cryptography==2.8
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- cycler==0.10.0
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- dill==0.3.1.1
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- distro==1.4.0
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- docker==4.2.0
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- docutils==0.15.2
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- dotnetcore2==2.1.13
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- flask==1.0.3
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- fusepy==3.0.1
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- gensim==3.8.1
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- google-api-core==1.16.0
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- google-auth==1.12.0
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- google-cloud-core==1.3.0
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- google-cloud-storage==1.26.0
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- google-resumable-media==0.5.0
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- googleapis-common-protos==1.51.0
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- gunicorn==19.9.0
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- idna==2.9
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- imbalanced-learn==0.4.3
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- isodate==0.6.0
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- itsdangerous==1.1.0
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- jeepney==0.4.3
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- jinja2==2.11.1
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- jmespath==0.9.5
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- joblib==0.14.0
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- json-logging-py==0.2
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- jsonpickle==1.3
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- jsonschema==3.0.1
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- kiwisolver==1.1.0
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- liac-arff==2.4.0
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- lightgbm==2.2.3
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- markupsafe==1.1.1
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- matplotlib==3.1.3
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- more-itertools==6.0.0
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- msal-extensions==0.1.3
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- msal==1.1.0
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- msrest==0.6.11
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- msrestazure==0.6.3
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- ndg-httpsclient==0.5.1
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- nimbusml==1.6.1
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- numpy==1.18.2
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- oauthlib==3.1.0
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- pandas==0.25.3
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- pathspec==0.7.0
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- pip==20.0.2
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- portalocker==1.6.0
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- protobuf==3.11.3
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- pyarrow==0.16.0
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- pyasn1-modules==0.2.8
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- pyasn1==0.4.8
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- pycparser==2.20
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- pycryptodomex==3.7.3
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- pyjwt==1.7.1
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- pyopenssl==19.1.0
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- pyparsing==2.4.6
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- pyrsistent==0.16.0
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- python-dateutil==2.8.1
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- pytz==2019.3
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- requests-oauthlib==1.3.0
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- requests==2.23.0
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- rsa==4.0
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- ruamel.yaml==0.15.89
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- s3transfer==0.3.3
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- scikit-learn==0.22.2
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- scipy==1.4.1
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- secretstorage==3.1.2
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- setuptools==46.1.1.post20200323
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- six==1.14.0
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- smart-open==1.10.0
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- urllib3==1.25.8
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- websocket-client==0.57.0
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- werkzeug==0.16.1
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- wheel==0.34.2
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To install other packages not in the pre-installed list, for example *scikit-misc*, add the following code to your script:
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‎articles/machine-learning/algorithm-module-reference/score-model.md

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After you have generated a set of scores using [Score Model](./score-model.md):
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+ To generate a set of metrics used for evaluating the model’s accuracy (performance). you can connect the scored dataset to [Evaluate Model](./evaluate-model.md),
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+ To generate a set of metrics used for evaluating the model's accuracy (performance), you can connect the scored dataset to [Evaluate Model](./evaluate-model.md),
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+ Right-click the module and select **Visualize** to see a sample of the results.
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+ Save the results to a dataset.
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<!-- + To Save the results to a dataset. -->
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The score, or predicted value, can be in many different formats, depending on the model and your input data:
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## Publish scores as a web service
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A common use of scoring is to return the output as part of a predictive web service. For more information, see this tutorial on how to create a web service based on a pipeline in Azure Machine Learning:
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A common use of scoring is to return the output as part of a predictive web service. For more information, see [this tutorial](https://docs.microsoft.com/azure/machine-learning/tutorial-designer-automobile-price-deploy) on how to deploy a real-time endpoint based on a pipeline in Azure Machine Learning designer.
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## Next steps
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