The Ultimate Guide To Supervised Learning | Classification And Regression | Part 2

Key Takeaways

supervised learning models are divided into two categories classification and regression the goal of classification is to predict categorical labels which are discrete unordered values in other words classify an observation into a fixed number of categories classification is divided into binary and multiclass classification tasks in the case of binary classification the aim is to decide between two choices for example predicting if the person has a disease or not classifying spam or not spam emails predicting rain or not rain for a day multiclass classification deals with multiple classes meaning more than two for example there is an imag net model trained on images of 1,000 classes other examples include sentiment analysis emotion prediction and others in the case of regression the labels are continuous ordered and not fixed for example predict the salary of a person based on his role experience and education salary can range from zero to infinity and can even have this form other examples include estimating house prices or predicting the future population of a certain area here are the ml algorithms used for classification and regression tasks note that some of them can be used for both the evaluation metrics are different for classification and regression tasks these are the criteria used for classification and these are used for regression more about the evaluation metrics can be found in the upcoming videos if you want to learn learn more about artificial intelligence subscribe to our channel to be aware of the new videos press the like button and let's discuss AI in the comments section