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There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved.
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‘Regression’ a word that is thought of with a lot of pain by software testers around the globe. We are aware of how mentally taxing yet indispensable regression testing can be for a release window. Sometimes, we even wonder whether regression testing is really needed? Why do we need to perform it when a bug-free software can never be ready? Well, the answer is yes! We need to perform regression testing on regular basis. The reason we do so is to discover regression defects. Wondering what regression defects are and how you can deal with them effectively? Well, in this article, I will be addressing key points for you to be aware of what regression defects are and how you can discover and handle regression defects for a successful release.
Often during software testing, some bugs are detected and a quick bug fix is carried out. Regression testing is executed in order to ensure that the bug fix didn’t cause any abruption in the application’s intended functionality. When some bugs are found to be occurring as a result of the bug fix, those are known as regression bugs. For example, let’s say your login page is having some errors and the developer fixed it. Now, the login page is working fine, but the registration page is causing some validation or other errors that did not exist earlier. The new error may have been caused because of the fix on the login page. This is a regression defect and it is relevant to deal with for delivering a more robust product in the market.