INTRODUCTION – Create A Regression Model With Azure Machine Learning Designer
Regression is a supervised machine learning technique whose main objective is to predict numerical values based on data inputs. This module introduces regression model development using Azure Machine Learning Designer-an easy and user-friendly tool for developing and deploying machine learning models.
Through hands-on training using the Designer, you will learn to create and refine regression models without extensive programming knowledge using the simple, intuitive drag-and-drop interface. With this module, you will acquire a good understanding of how Azure Machine Learning Designer can be put to effective use in producing regression models; thereby guiding you through real predictive analytics problems 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 that develops a structured model using input data and the corresponding label values. It then learns how to fit the combinations of feature data into the label so that the new unseen data will be predicted accurately.
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: With this workspace, you organize your whole workflow in the way that it manages your data, computational resources, code, models, and other artifacts pertaining to your machine learning projects.
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 refer to scalable arrays of virtual machines which avails on-demand processing power for running code in experiments and therefore enables efficient management of large-scale computations with respect to machine learning workflows.
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: A regression model can be trained if you have available data containing historical records concerning input features and known output labels of the respective targets. Such a dataset allows the model to learn how inputs relate with their corresponding targets, thus allowing it to make predictions based on unfamiliar data.
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 is a scalable cloud computational facility for processing large amounts of data. Hence, the service charges only for Azure Machine Learning resources used, making the cost-effective service available for machine learning workload.
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 comprehensive platform available on the cloud and has several tools and features that aid data scientists in preparing data, training predictive models, deploying services, and monitoring their performance.
Correct: The azure machine learning enables the users to prepare, train, deploy and monitor from a huge diverse set of features offered with cloud base services.
Correct: Azure Machine Learning is a cloud service that provides a full range of services and features required for data preparation, model training, deployment of predictive services, and monitoring their usage for data scientists.
Correct: It is an online environment which provides several features and different tools such as, data preparation, model training, deployment of prediction services, and then monitoring those services’ performance for the data scientists.
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 serves as the theoretical base for a predictively modeled service, wherein it can be published and brought into use by applications.
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: These are the four types of compute resources that one can have in Azure Machine Learning Studio- Compute Instances; Compute Clusters; Inference Clusters; and Attached Compute.
Correct: Within Azure Machine Learning Studio, compute instances—four classes exist of compute resource facilities: Compute Clusters, Inference Clusters, and Attached Compute.
Correct: Four kinds of computing facilities serving within Azure Machine Learning Studio are Compute Instances, Compute Clusters, Inference Clusters, and Attached Compute.
Correct: These are the four different types of compute resources available in Azure Machine Learning Studio: Compute Instance, Compute Cluster, Inference Cluster, 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 select a subset of columns to be consumed by future operations through the module.
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: Using this module, subsequent operations can be performed for the selection of certain columns.
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 statistical method of machine learning where one predicts a phenomenon in numerical value from the feature of the item under study.
Correct: Regression has a stuff like machine learning. It is used to predict a numerical label for the features of an item.
Correct: Regression cannot be defined as a type of machine learning in predicting a number in label by features for an object.
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 serve as deployment targets for predictive services utilizing 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 one of the supervised techniques the machine uses to train a model to specify or obtain numeric labels corresponding to features of an item.
CONCLUSION – Create A Regression Model With Azure Machine Learning Designer
This refers to an important supervised machine learning technique that is really great at predicting numeric values from inputs. As an example, this will entail learning how to create and optimize regression models with the intuitive interface of the Azure Machine Learning designer.
With this hands-on experience, you can now use Microsoft’s state-of-the-art machine-learned Azure architecture to create powerful regression models against real-life problems in predictive analysis with greater ease and confidence.