COURSE 2: MICROSOFT AZURE MACHINE LEARNING FOR DATA SCIENTIST

Module 2: Create A Regression Model With Azure Machine Learning Designer

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Last updated:

July 10, 2024

INTRODUCTION – Create A Regression Model With Azure Machine Learning Designer

Regression, a key supervised machine learning technique, is employed to predict numeric values based on input data. This module will guide you through the process of creating regression models using the Azure Machine Learning designer, a powerful tool that simplifies the development and deployment of machine learning models.

You will gain hands-on experience with the designer’s intuitive drag-and-drop interface, allowing you to construct and fine-tune regression models without the need for extensive coding. By the end of this module, you will have a solid understanding of how to leverage Azure Machine Learning designer to build effective regression models, enabling you to tackle real-world predictive analytics challenges with confidence.

PRACTICE QUIZ: KNOWLEDGE CHECK

1. True or False?

Regression is a form of machine learning that is used to predict an item’s feature based on the numeric label.

True
False (CORRECT)

Correct: Regression is a machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

2. In order to use Azure Machine Learning, what should you create in your Azure subscription?

A SQL Server
An App Service Plan
A Machine Learning Workspace (CORRECT)

Correct: You can use this workspace to manage data, compute resources, code, models, and other artifacts related to your machine learning workloads.

3. Suppose you created a machine learning model and you want to train it. Which compute target should you use?

Compute Instances.
Inference Clusters
Compute Clusters (CORRECT)

Correct: Compute Clusters are scalable clusters of virtual machines for on-demand processing of experiment code.

4. True or False?

To train a regression model, you need a dataset that includes historical features and known label values.

True (CORRECT)
False

Correct: To train a regression model, you need a dataset that includes historical features and known label values.

5. You are planning on using Azure Machine Learning. From a payment plan perspective, what costs should you expect?

Monthly subscription
One-time license
Pay-for-what-you-use (CORRECT)

Correct: Azure Machine Learning enables the use of cloud-based compute resources that scale effectively to handle large volumes of data. With this cloud-based service you only pay for the Azure Machine Learning resources that you use.

QUIZ: TEST PREP

1. What features and capabilities are available in Azure Machine Learning?

Select all that apply.

Publish predictive services (CORRECT)
Train models (CORRECT)
Monitor usage of used services (CORRECT)
Prepare data (CORRECT)

Correct: Azure Machine Learning is a cloud-based service with a wide range of features and capabilities that help data scientists to prepare data, train models, publish predictive services, and monitor their usage.

2. True or False?

After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to predict label values based on its features.

True (CORRECT)
False

Correct: An inference pipeline will form the basis for a predictive service that you can publish for applications to use.

3. What type of compute resources can be created in Azure Machine Learning Studio?

Spot clusters
Compute instances (CORRECT)
Attached compute (CORRECT)
Inference clusters (CORRECT)
Compute clusters (CORRECT)

Correct: The four types of compute resources available in Azure Machine Learning Studio are: Compute instances, Compute Clusters, Inference clusters and Attached Compute.

Correct:The four types of compute resources available in Azure Machine Learning Studio are: Compute instances, Compute Clusters, Inference clusters and Attached Compute.

4. You are creating a training pipeline for a regression model and you want to make sure that the dataset is complete, otherwise you need to perform various operations to fix the data. Which module should you add to the pipeline?

Select columns in a dataset
Clean missing data (CORRECT)
Normalize data

Correct: This module is used to choose a subset of columns to use in downstream operations.

5. You are creating a training pipeline for a regression model and your dataset contains hundreds of columns. For a particular part of your model, you want to use data only from some specific columns. Which module should you add to the pipeline?

Select columns in a dataset (CORRECT)
Normalize data
Clean missing data

Correct: This module is used to choose a subset of columns to use in downstream operations.

6. Which of the following scenarios can be resolved by using a regression model?

Determine if patients with some pre-existing conditions are more likely to suffer from diabetes
Predict yearly income of customers based on their occupation, age, education etc. (CORRECT)
Predict selling price of a car using data like engine size, mileage, number of seats etc. (CORRECT)
Predict daily rental demand of bicycles by using historic data. (CORRECT)

Correct: Regression is a form of machine learning that is used to predict a numeric label based on an item’s features

7. You created a machine learning model and trained it. Now you want to run the model to predict data. Which compute target should you use?

Compute Clusters
Inference Clusters (CORRECT)
Compute Instances

Correct: Inference Clusters are used as deployment targets for predictive services that use your trained models.

8. Do you think the following statement is true for Regression?

Regression is an example of a supervised machine learning technique in which you train a model to predict a numeric label based on an item’s features.

Yes (CORRECT)
No

Correct: Regression is an example of a supervised machine learning technique in which you train a model to predict a numeric label based on an item’s features.

CONCLUSION – Create A Regression Model With Azure Machine Learning Designer

In conclusion, regression is a fundamental supervised machine learning technique crucial for predicting numeric values from input data. Through this module, you have learned to create and refine regression models using the Azure Machine Learning designer’s user-friendly interface.

With hands-on experience, you are now equipped to harness the capabilities of Azure Machine Learning to build effective regression models, addressing real-world predictive analytics challenges with greater ease and confidence.

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