Learn how to use Azure Machine Learning Designer’s Split Data model to create a training pipeline for a binary classification model. Splitting data into training and testing sets is essential to ensure that the model is not overfitting or underfitting the data.
Table of Contents
Question
You are using Azure Machine Learning designer to create a training pipeline for a binary classification model.At some point, you want to separate the data into training and testing sets. Which model should you add to the pipeline?
A. Join data
B. Split data
C. Select columns in dataset
Answer
B. Split data
Explanation
If you want to separate the data into training and testing sets while creating a training pipeline for a binary classification model using Azure Machine Learning designer, you should add the Split Data model to the pipeline. The Split Data model is used to split the dataset into two parts: one for training the model and the other for testing the model. This model is used to ensure that the model is not overfitting or underfitting the data.
The Join Data model is used to combine two datasets into one. The Select Columns in Dataset model is used to select specific columns from a dataset. Therefore, neither of these models would be appropriate for separating the data into training and testing sets.
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