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Exam DP-100 topic 3 question 51 discussion

Actual exam question from Microsoft's DP-100
Question #: 51
Topic #: 3
[All DP-100 Questions]

HOTSPOT -
Your Azure Machine Learning workspace has a dataset named real_estate_data. A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

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Suggested Answer:
Box 1: training_data -
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.

Box 2: validation_data -
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation. dataset = Dataset.Tabular.from_delimited_files(data) training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class'
)

Box 3: label_column_name -
label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Incorrect Answers:
X: The training features to use when fitting pipelines during an experiment. This setting is being deprecated. Please use training_data and label_column_name instead.
Y: The training labels to use when fitting pipelines during an experiment. This is the value your model will predict. This setting is being deprecated. Please use training_data and label_column_name instead.
X_valid: Validation features to use when fitting pipelines during an experiment.
If specified, then y_valid or sample_weight_valid must also be specified.
Y_valid: Validation labels to use when fitting pipelines during an experiment.
Both X_valid and y_valid must be specified together.
exclude_nan_labels: Whether to exclude rows with NaN values in the label. The default is True. y_max: y_max (float)
Maximum value of y for a regression experiment. The combination of y_min and y_max are used to normalize test set metrics based on the input data range. If not specified, the maximum value is inferred from the data.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py
by treadst0ne at June 20, 2021, 1:28 a.m.

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treadst0ne
Highly Voted 3 years, 4 months ago
Answer is correct. https://docs.microsoft.com/en-us/learn/modules/automate-model-selection-with-azure-automl/4-automl-experiments
upvoted 15 times
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Matt2000
Most Recent 8 months, 2 weeks ago
Answer is correct. By the way, X, Y, X_valid, Y_valid are being deprecated. Use training_data and label_column_name or alternatively validation_data and label_column_name instead. Reference: https://learn.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py
upvoted 1 times
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orionduo
1 year, 2 months ago
from azureml.train.automl import AutoMLConfig automl_run_config = RunConfiguration(framework='python') automl_config = AutoMLConfig(name='Automated ML Experiment', task='classification', primary_metric = 'AUC_weighted', compute_target=aml_compute, training_data = train_dataset, validation_data = test_dataset, label_column_name='Label', featurization='auto', iterations=12, max_concurrent_iterations=4)
upvoted 1 times
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RoohiSaanjh
2 years, 7 months ago
If specified, then y_valid or sample_weight_valid must also be specified. This setting is being deprecated. Please use validation_data and label_column_name instead. Both X_valid and y_valid must be specified together. This setting is being deprecated. Please use validation_data and label_column_name instead. https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py
upvoted 1 times
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hargur
3 years ago
on 19Oct2021
upvoted 2 times
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VJPrakash
3 years, 2 months ago
on exam in August 2021
upvoted 1 times
...
ljljljlj
3 years, 3 months ago
On exam 2021/7/10
upvoted 1 times
...
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