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2 changes: 1 addition & 1 deletion doc/index.rst
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Expand Up @@ -30,7 +30,7 @@ Example
('estimator', tree.DecisionTreeClassifier())
]
)
# Download the OpenML task for the german credit card dataset with 10-fold
# Download the OpenML task for the pendigits dataset with 10-fold
# cross-validation.
task = openml.tasks.get_task(32)
# Run the scikit-learn model on the task.
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6 changes: 5 additions & 1 deletion examples/30_extended/custom_flow_.py
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Expand Up @@ -77,6 +77,8 @@
# you can use the Random Forest Classifier flow as a *subflow*. It allows for
# all hyperparameters of the Random Classifier Flow to also be specified in your pipeline flow.
#
# Note: you can currently only specific one subflow as part of the components.
#
# In this example, the auto-sklearn flow is a subflow: the auto-sklearn flow is entirely executed as part of this flow.
# This allows people to specify auto-sklearn hyperparameters used in this flow.
# In general, using a subflow is not required.
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autosklearn_flow = openml.flows.get_flow(9313) # auto-sklearn 0.5.1
subflow = dict(
components=OrderedDict(automl_tool=autosklearn_flow),
# If you do not want to reference a subflow, you can use the following:
# components=OrderedDict(),
)

####################################################################################################
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OrderedDict([("oml:name", "time"), ("oml:value", 120), ("oml:component", flow_id)]),
]

task_id = 1965 # Iris Task
task_id = 1200 # Iris Task
task = openml.tasks.get_task(task_id)
dataset_id = task.get_dataset().dataset_id

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