Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

© Timothy Masters 2018

Timothy MastersAssessing and Improving Prediction and Classificationhttps://doi.org/10.1007/978-1-4842-3336-8_4

4. Resampling for Assessing Prediction and Classification

Timothy Masters¹ 

(1)

Ithaca, New York, USA

 

• Partitioning the Error

• Cross Validation

• Bootstrap Estimation of Population Error

• Efron’s E0 and E632 Estimates of Error

• Comparing the Error Estimators for Prediction and Classification

The most common procedure for assessing the performance of a prediction or classification model is to split the data collection into two subsets. The model is trained with one dataset, the training set , and then tested with a completely independent dataset, the test set or validation set or out-of-sample set . (The choice of term ...