You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?
Click on the arrows to vote for the correct answer
A. B. C. D.C
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splitsThe correct answer is C. Split Data.
Azure Machine Learning Designer is a drag-and-drop interface that allows you to create, test, and deploy machine learning models without any coding. It provides several modules to perform various operations on datasets, including selecting columns, adding rows, splitting data, and joining data.
In this case, you want to create a training dataset and a validation dataset from an existing dataset. The process of splitting a dataset into two or more parts is known as data splitting. This is a common technique in machine learning, where you use one part of the data to train the model and another part to evaluate its performance.
The Split Data module in Azure Machine Learning Designer allows you to split a dataset into two or more parts based on a specific criteria. You can split the data randomly, based on a column value, or using a custom expression. The module outputs two datasets: the first dataset contains the training data, and the second dataset contains the validation data.
Therefore, to create a training dataset and a validation dataset from an existing dataset in Azure Machine Learning Designer, you should use the Split Data module.