Create a Regression Model with Azure Machine Learning designer

Introduction

This tutorial will guide you through the process of creating a regression model using the Azure Machine Learning designer. Regression analysis is a fundamental technique in supervised machine learning that allows us to predict numeric values based on input data. By the end of this guide, you'll have a clear understanding of how to leverage Azure's tools for your regression tasks.

Step 1: Set Up Your Azure Machine Learning Workspace

• Create an Azure Account: If you don’t have one, sign up for an Azure account. Microsoft often offers free credits for new users.
• Access Azure Machine Learning: Navigate to the Azure portal and create a new Azure Machine Learning workspace.
• Launch the Designer: Go to the Azure Machine Learning workspace and select ‘Launch Studio’. From there, you can access the Machine Learning designer.

Step 2: Create a New Pipeline

• Select Designer: In the Azure Machine Learning Studio, find and select the ‘Designer’ option.
• Start a New Pipeline: Click on ‘New Pipeline’ to begin creating your regression model.
• Choose Dataset: Import the dataset you will be using for your regression model. You can either upload a file or select from existing datasets in your workspace.

Step 3: Data Preparation

• Data Exploration: Before modeling, explore the dataset. Use the ‘Data Preview’ feature to understand the data distribution and identify any missing values.
• Clean the Data: If necessary, use data transformation tools available in Azure to handle missing values or outliers.
• Select Features: Choose the relevant features that will be used for regression. This is crucial as it impacts the model's performance.

Step 4: Choose a Regression Algorithm

• Select Algorithm: In the designer interface, drag and drop a regression algorithm onto the canvas. Common algorithms include:
  • Linear Regression
  • Decision Forest Regression
  • Boosted Decision Tree
• Configure the Algorithm: Click on the algorithm node to configure its parameters based on your analysis needs.

Step 5: Connect the Components

• Link Nodes: Connect the dataset node to the data preparation node, and then connect the preparation node to the regression algorithm node.
• Add a Score Model Node: After the regression node, add a ‘Score Model’ node to evaluate your model's performance.

Step 6: Train the Model

• Add Train Model Node: Insert a ‘Train Model’ node and connect it to your regression algorithm.
• Set the Target Variable: Specify the target variable that you want to predict.
• Run the Pipeline: Click on 'Run' to train the model using the dataset.

Step 7: Evaluate Model Performance

• Add Evaluate Model Node: Connect an ‘Evaluate Model’ node to assess the model’s performance metrics.
• Review Results: After running the pipeline, review the evaluation metrics such as R-squared, Mean Absolute Error (MAE), and others to understand how well your model is performing.

Step 8: Deploy the Model

• Register the Model: If satisfied with the performance, register the model in your Azure workspace for future use.
• Create an Endpoint: Set up a web service endpoint for your model so it can be accessed by applications or users for making predictions.

Conclusion

In this tutorial, you have learned how to create a regression model using Azure Machine Learning designer. By setting up your workspace, preparing your data, choosing and configuring algorithms, and evaluating model performance, you are now equipped to predict numeric values with machine learning. As next steps, consider experimenting with different algorithms or integrating your model with applications for real-world use cases. Happy modeling!