How to Evaluate the Performance of Your ML/ AI Models

Learning by doing is one of the best approaches to learning anything, from tech to a new language or cooking a new dish. Once you have learned the basics of a field or an application, you can build on that knowledge by acting. Building models for various applications is the best way to make your knowledge concrete regarding machine learning and artificial intelligence. Though both fields (or really sub-fields, since they do overlap) have applications in a wide variety of contexts, the steps to learning how to build a model are more or less the same regardless of the target application field. AI language models such as <a href="https://openai.com/blog/chatgpt" rel="noopener ugc nofollow" target="_blank">ChatGPT</a> and <a href="https://bard.google.com/" rel="noopener ugc nofollow" target="_blank">Bard</a> are gaining popularity and interest from both tech novices and general audiences because they can be very useful in our daily lives. Now that more models are being released and presented, one may ask, what makes a “good” AI/ ML model, and how can we evaluate the performance of one? This is what we are going to cover in this article. But again, we assume you already have an AI or ML model built. Now, you want to evaluate and improve its performance (if necessary). But, again, regardless of the type of model you have and your end application, you can take steps to evaluate your model and improve its performance. <a href="https://towardsdatascience.com/how-to-evaluate-the-performance-of-your-ml-ai-models-ba1debc6f2fa">Click Here</a>