How to Evaluate Your ML Models Effectively? | Evaluation Metrics in Machine Learning!

🔥 In this video we refer to the evaluation metrics used in machine learning. Confusion matrix, Accuracy, Precision, Recall and F1-Score are the most popular metrics for classification tasks. We explain the difference of each metric on a single example, showing that accuracy is well suited for balanced datasets, while other three for imbalanced ones. In some specific cases, we may prefer recall over precision and vice versa, or we might want to have both high using F1-Score. Additionally, there are other important metrics like AUC and ROC. Metrics for unsupervised learning and regression tasks are different. These are more complex topics, which we will cover separately, so stay with us! 🔍 Key points covered: 0:00 - Introduction to the problem. 0:20 - Understanding the confusion matrix. 0:45 - Accuracy. 0:59 - When not to use the accuracy? 1:35 - Recall and Precision. 1:45 - Precision. 1:52 - Recall. 2:02 - F1-Score. 2:17 - How to choose between the metrics? 2:25 - Important notes. 2:45 - Subscribe to us! 🔔 Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos! 🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content. 🌐 If you have any questions or topics you want us to cover, leave a comment below. Additionally, share with your thoughts about the content, how do you think we can make them better? Thanks for watching!