Introduction to Machine Learning IML5: Regression vs Classification скачать в хорошем качестве

Introduction to Machine Learning IML5: Regression vs Classification

I present a brief overview of the difference between regression and classification problems. -I briefly discuss that in supervised learning regression refers to the output features being a continuous number such as age, house price or academic grades. -Classification however, refers to the output being a categorical value such as male/female or pass/fail or even multiple classes. Also, these problems ONLY refer to what is happening with the output feature, NOT the input predictor features. For example, we can have both continuous or categorical input features for either a regression or classification problem