贝叶斯决策理论(英文)--非常经典!.pdf

Classification vs. Regression Classification predicts categorical class labels Prediction Regression models continuous-valued functions, i.e. predicts numerical values Two step process of prediction (I) Step 1: Construct a model to describe a training set • the set of tuples used for model construction is called training set • the set of tuples can be called as a sample (a tuple can also be called as a sample) • a tuple is usually called an example (usually with the label) or an instance (usually without the label) • the attribute to be predicted is called label Training algorithm Training Data label Name Rank Years Tenured Mike Assistant Prof 3 no Prediction Mary Assistant Prof 7 yes model Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no e.g., IF rank = professor OR Anne Associate Prof 3 no years 6 THEN tenured = yes Two step process of prediction (II) Step 2: Use the model to predict unseen instances before use the model, we can estimate the accuracy of the model by a test set • test set is different from training set • the desired output of a test instance is compared with the actual output from the model • for classification, the accuracy is usually measured by the percentage of test instances that are correctly classified by the model • for regression, the accuracy is usually measured by mean squared error