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The document outlines key concepts in supervised learning, including definitions and examples of classification and regression, as well as the roles of datasets, features, and labels. It discusses the importance of separating training, test, and validation data, and addresses issues of overfitting and underfitting, along with methods to prevent them. Additionally, it defines Mean Squared Error (MSE) and Mean Absolute Error (MAE), explaining their computations, properties, and contexts for use, while also co
- Author
- t66ju4w
- Language
- EN