Machine Learning Models
A brief about Machine Learning
Testing of Machine Learning Models, Machine Learning is a subset of Artificial Intelligence. AI is a buzzword now in the technology industry. As the name suggests machine learning is something that learns from a given data and provides output according to the data. Here mostly the data is divided into test data and training data and accordingly the algorithms are run.
So here basically nothing has explicitly programmed the machine automatically learns from scenarios and then behaves in a certain way. Machine learning has its own categorizations. They are basically divided into three categories. They are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. This has its own uses in various data sets and changes according to business needs.
A brief about testing with Machine Learning Models
So now we understand what machine learning is all about. Like any other project machine learning projects also involve the main phases of requirements gathering where the pure business requirements are understood. Then comes the development phase where mainly exploratory data analysis and development of different models take place. Then after development testing should be done, where we check the accuracy of each model. The model doesn’t make sense if the accuracy is not up to a certain level as per business needs and decisions. Once proper testing is done and verified the model is deployed and integrated with the main framework.
What is Black Box Testing?
Black box testing generally comes under functional and system testing. Both functional and system testing is the core of any testing service. So is black-box testing. Black box testing is really essential to meet the client’s requirements. This testing has to be done really carefully taking all parameters into consideration. This testing mostly deals with error handling of the system. It ensures that errors are minimal in the count. This testing mostly deals with the user interface of the system. It also closely deals with user inputs and outputs. This testing makes sure that everything is working correctly.
Role of Black Box Testing in Machine Learning
Black box testing is really important in any kind of project. In machine learning, black-box testing holds its own set of advantages. Black box testing is really essential to meet the client’s requirements and also to understand the nature of the dataset. This testing has to be done really carefully taking all parameters into consideration. This testing mostly deals with the handling of data cleaning and ensuring all parameters are present. It ensures that errors are minimal in the count. It closely deals with user inputs and outputs. This testing makes sure that the model is giving a high accuracy value and is in line with the business need and decision
A brief about the testing of data in Machine Learning Models
When it comes to machine learning one should be very sure that data is the main element of it. Data is the crux of any machine learning project that is taken. Having mentioned the importance of data one should make sure that data is being handled very carefully. Testing of data holds a lot of importance in the Machine Learning world. One should really pay great attention to what kind of data is present in the model. One should be aware of null values and outliers that might decrease the accuracy of data in the end. This would not help us in making a good machine learning model.
A brief about testing the features in Machine Learning Models
When talked about, Machine learning features are the second most important part of the project. Features are often called variables or parameters. Features more or less decide the accuracy of the model and also helps in elevating model performance. To be able to test if the correct features are present in the dataset or not one should have quite an amount of business knowledge. If the business knowledge is proper then choosing correct parameters becomes an easy job. One should be aware of the business context so as to know which variables to consider to build a good machine learning model.
A brief about testing the algorithms in Machine Learning Models
Algorithms are the ways or techniques in which a machine learning model behaves. For this, it is very important that people know a good amount of statistics. Machine learning algos can be written in many languages and widely used is python and r. Algorithms are nothing but a set of steps or procedures that should be followed to get the desired output. Testing of all these algorithms is a skill in itself. For this one should know advanced statistics and should be well versed with it. One should know when to use the techniques so that the model performance is of high quality and also the accuracy is according to the business need and decision.
A brief about testing model performance
Everything ends with measuring the result and return on investment of the project. One should be able to provide results in line with what the client has asked for. The performance of the model is something that actually measures whether it can be actually deployed in the main framework or not. So now we can sense how important testing of model performance is. If this part is not taken seriously the entire project might fail in the end. One should also know how to do model performance testing. For this again one should know what statistics, should have knowledge about data. One should also know what algorithms should be used. One should also have a good hold on the business context and data flow of the given project.
Best tools for Machine Learning Model Testing
For manual testing of a Machine learning the tools which can be used to develop a machine learning model can also be used for testing the model. The tools are –
WEKA- Waikato Environment for Knowledge Analysis, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License and the companion software to the book “Data Mining: Practical Machine Learning Tools and Techniques”.
PyCharm- PyCharm is an integrated development environment used in computer programming, specifically for the Python language. It is developed by the Czech company JetBrains
Spyder- Spyder is an open-source cross-platform integrated development environment for scientific programming in the Python language.
However, there are tools which can be for automating testing concerning Artificial Intelligence –
Testim.io- Testim.io leverages machine learning for the authoring, execution, and maintenance of automated test cases. We use dynamic locators and learn with every execution. The outcome is super fast authoring and stable tests that learn, thus eliminating the need to continually maintain tests with every code change
Appvance- The company’s premier product is Appvance IQ, the world’s first AI-driven, unified test automation system. It helps enterprises dramatically improve the quality
Test.ai- Test.ai (formerly Appdiff) is the world’s first truly AI-powered test automation solution that improves the speed and quality of testing to meet the demands of clients
Functionize- Functionize delivers an intelligent testing platform that incorporates AI and machine learning technologies to automate the painstaking software testing process
What are the skills needed to do testing of machine learning models
The skills needed to be good at testing machine learning models are quite a lot. One should know that to be a good quality analyst in this field one should know machine learning as well as end to end Quality Assurance. Number one is that one should know statistics, and be well versed with the basic level of data handling. To add to this a quality analyst should also know how to use algorithms and the logic behind that. One is not expected to be a pro-level is all of this but basic knowledge is what is expected. Business knowledge will be of great use during the entire process of testing a machine learning model. The best practices of quality assurance should be known. Knowledge of all the tools that are used to test machine learning models is necessary. A good team player and should have some analytical skills as well.
What is the importance of Machine Learning Model Testing?
The importance of testing a machine learning model is plenty in number. This testing is also essential because the demand for machine learning, artificial intelligence, and data science has increased exponentially. Since the importance of these subjects has increased and people have started to realize the value of this all companies are ready to spend on this technology. This is quite a lengthy project and also a complex one. This requires a lot of attention to make sure that the accuracy of the model is well above average and the performance of the model is good. All these things make testing of the machine learning model quite essential and a must-have before deploying it to the main framework.
So this article covers end to end of testing of machine learning models. This article covered the various aspects of any project that is related to machine learning. It also helped us understand the importance of testing in this field of the technology world. This article covered what are the skills that are required to stay ahead in this field. This has also helped us know about the top tools that are available in the market.