You need to create a new pipeline to train a regression model using Azure ML Designer. You ingest your data for the model and drop it on the canvas. What module would you typically drag-and-drop next on the canvas?Response: Select Columns in Dataset Train ModelClean Missing dataNormalize DataSplit Data
Question
You need to create a new pipeline to train a regression model using Azure ML Designer. You ingest your data for the model and drop it on the canvas. What module would you typically drag-and-drop next on the canvas?Response: Select Columns in Dataset Train ModelClean Missing dataNormalize DataSplit Data
Solution
After ingesting your data for the model and dropping it on the canvas, the next module you would typically drag-and-drop on the canvas is "Clean Missing Data". This module is used to handle missing data in the dataset. It can replace missing values with a specified replacement value or use a chosen method to estimate the missing values from the existing values.
After cleaning the missing data, you would typically use the "Split Data" module to divide your dataset into a training set and a testing set.
Then, you would use the "Select Columns in Dataset" module to choose the specific columns you want to use as features for your model.
The "Normalize Data" module is used to scale the features to a standard range of values.
Finally, you would use the "Train Model" module to train your regression model.
So, the typical order of modules would be:
- Clean Missing Data
- Split Data
- Select Columns in Dataset
- Normalize Data
- Train Model
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