AI-900: What Azure ML Designer Module Splits Datasets for Model Training and Validation?

Learn which Azure Machine Learning designer module to use for splitting an existing dataset into training and validation sets. The Split Data module is the correct choice for this essential step in the machine learning process.

Question

To train your model, 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?

A. Add Rows.
B. Join Data.
C. Split Data.
D. Select Columns in Dataset.

Answer

C. Split Data.

Explanation

When building machine learning models, it’s crucial to split your data into separate datasets for training and validation. This allows you to train the model on one portion of the data and then evaluate its performance on unseen data to assess how well it generalizes.

In Azure Machine Learning designer, the Split Data module is specifically designed for this purpose. Here’s how it works:

1. The Split Data module takes an existing dataset as input.
2. You specify the fraction or percentage of rows to go into the first output dataset, which is typically used for training. For example, you might allocate 70-80% of the data for training.
3. The remaining rows go into the second output dataset, which serves as the validation set. This is used to evaluate the trained model’s performance on data it hasn’t seen during training.
4. You can choose to split the data randomly or use a stratified split if working with classified data to ensure each output has a representative sample of each class.
5. The resulting training and validation datasets can then be connected to the appropriate downstream modules for model training and evaluation.

The other module options mentioned are used for different tasks:

• Add Rows appends one dataset to another
• Join Data combines two datasets based on a key column
• Select Columns in Dataset allows choosing a subset of columns

But none of these split a single dataset into two for training and validation like the Split Data module does.

So in summary, whenever you need to create separate training and validation sets in Azure ML designer, the Split Data module is the go-to tool for the job. It’s an essential step in building robust, properly evaluated machine learning models.

The Split Data module in Azure Machine Learning Designer is used to create a training dataset and validation dataset from an existing dataset. It allows you to specify the proportion of data for each subset, ensuring effective model training and valication.

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