HOTSPOT - You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment. You need to configure the module. You must meet the following requirements: ✑ Divide the data into subsets ✑ Assign the rows into folds using a round-robin method ✑ Allow rows in the dataset to be reused...

Questions › Category: DP-100 › HOTSPOT – You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment. You need to configure the module. You must meet the following requirements: ✑ Divide the data into subsets ✑ Assign the rows into folds using a round-robin method ✑ Allow rows in the dataset to be reused…

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HOTSPOT -
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and
Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
✑ Divide the data into subsets
✑ Assign the rows into folds using a round-robin method
✑ Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Suggested Answer:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
1. Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
2. For Partition or sample mode, select Assign to Folds.
3. Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.
4. If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.
5. Randomized split: Select this option if you want rows to be randomly assigned to folds.
If you do not select this option, rows are assigned to folds using the round-robin method.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate

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