A large amount of time for machine learning tasks is spent understanding the data and getting it into the proper configuration to train the model. This is the Data Wrangling, Exploration, and Cleaning phase of the machine learning lifecycle. In Azure Machine Learning Designer, many common data changing operations are provided as transform modules.
In this lab, you will explore the `Add Columns` module to gain a deeper understanding of the tools at your disposal.
Learning Objectives
Successfully complete this lab by achieving the following learning objectives:
- Set Up the Workspace
Log in and go to the Azure Machine Learning Studio workspace provided in the lab.
Create a Training Cluster of
D2instances.Create a new blank Pipeline in the Azure Machine Learning Studio Designer.
- Explore Add Columns
Add CRM Dataset Shared and CRM Upselling Labels Shared dataset nodes to the canvas. Visualize each node to see the raw dataset. Take note of the row and column counts.
These two datasets are the same size, and already ordered. Use an Add Columns node to combine these into a single dataset.
Submit the Pipeline to perform the transformation.
- Visualize the Transformed Data
When the pipeline finishes, inspect the output of the Add Columns node. How many rows and columns are now present in the dataset?
