How should you configure the module?

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.

View Answer

Answer:

Explanation:

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.

✑ Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.

✑ For Partition or sample mode, select Assign to Folds.

✑ 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.

✑ 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.

✑ 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