Comparing models in Azure Machine Learning

Introduction

After discussing a few algorithms and techniques with Azure Machine Learning let us discuss techniques of comparison in Azure Machine Learning in this article. During this series of articles, we have discussed the basic cleaning techniques, feature selection techniques and Principal component analysis, etc. After discussing Regression and Classification analysis let us focus more on performing comparison in Azure Machine Learning.

Evaluation in Azure Machine Learning

As we discussed during previous articles, we discussed limited options of evaluation using Score Model and Evaluate Model control. However, for real-world implementation score model and evaluate model controls might not be needed. Therefore, let us look at how to perform compare multiple models and different model parameters in Azure Machine Learning.

Evaluate Model

Though we used Evaluate Model control to evaluate in the previous article, we did not use it to compare multiple models or configurations. Before looking at comparing models, let us quickly create an experiment in Azure Machine Learning to start with.

By using the AdventureWorks sample dataset the following Azure Machining Learning experiment is created as shown in the below figure.

Let us quickly go through how the above experiment was created as we have done this multiple times in previous articles. As we have 28 attributes in the sample dataset, we need to reduce them to a dataset that will be valid for the prediction. From the Select Columns in Dataset, you can select the 13 attributes for the model. Then, we will perform a data split by 70/30 to train the model and the test model respectively. Then the Multiclass Decision Forest classification technique was used to train the model. In the Train Model control, the BikeBuyer column is selected as the predictable column and then by using Score Model and Evaluate Model evaluation was done. For the Regression model, the predictable column was changed to the YearlyIncome.

If you remember, when we need to compare models, we used multiple experiments therefore we had to compare them manually.

Let us say we want to compare, between Multiclass Decision Forest and Multiclass Decision Jungle classification techniques. For that, the following experiment is created.

In the above model, what we have done is, another Train Model is added and the train output of the Split Data is connected to the new Train Model. As shown in the above figure, the Multiclass Decision Jungle and the output of the Train Model are linked to the Score model along with the Test date from the Split Data control. Finally, the output of the Score Model is linked to the available Evaluate Model.

Now, let us look at the evaluate output from the Evaluate Model by right-clicking the control and selecting the Visualize option.

In the evaluation results, you will have the evaluation parameters for both techniques. By comparing these two techniques on one screen, you can decide what the better technique is much easier.

Further, the output of the Evaluation Model will be different when you use different types of techniques. Let us look at what will be the output if we use Two Class classification techniques instead of Multi-Class classification techniques.

Let us look at the output of the Evaluation Model when Two-Class Decision Forest Model, Two-Class Decision Jungle algorithms are used.

As we discussed in the Classification Techniques, ROC, Precision/Recall and Lift charts are used to evaluate in two-class classification.

#azure #machine learning

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Azure Machine Learning Service

In a series of blog posts, I am planning to write down my experiences of training, deploying and managing models and running pipelines with Azure Machine Learning Service. This is part-1 where I will be walking you through the creation of workspace in Azure ML service

About Azure Machine Learning Service

Azure Machine Learning Service is a cloud based platform from Microsoft to train, deploy, automate, manage and track ML models. It has a facility to build models by using drag-drop components in Designer along with traditional code based model building. Azure ML service makes our job very ease in maintaining developed models and also helps in hassle free deployment of models in lower(QA, Unit) and higher(Prod) environments as APIs. It is integrated with various components in Azure like Azure Kubernetes Services, **Azure Databricks, Azure Monitor, Azure Storage accounts, Azure Pipelines, MLFlow, Kubeflow **to carry out various activities which will be discussed in upcoming posts.

Why Azure Machine Learning Service

In the process of building models, one need to play around with various hyperparameters and use various techniques. Also one need to scale out the resources for training the model if the dataset is huge. Bringing your model development and deployment to cloud makes your job easy. In particular Azure Machine Learning Service has below advantages.

1. Simplifies model management
2. Automated machine learning simplifies model building
3. Scales out training to GPU cluster or CPU cluster or Azure Databricks whenever needed with inbuilt integration
4. Deployment of models to production with Azure Kubernetes Service or Azure IOT edge is very simple.

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