Prediction and Internal Statistical Cross Validation

Abstract

Cross-validation is a statistical approach for validating predictive methods, classification models, and clustering techniques. It assesses the reliability and stability of the results of the corresponding statistical analyses (e.g., predictions, classifications, forecasts) based on independent datasets. For prediction of trend, association, clustering, and classification, a model is usually trained on one dataset (training data) and subsequently tested on new data (testing or validation data). Statistical internal cross-validation uses iterative bootstrapping to define test datasets, evaluates the model predictive performance, and assesses its power to avoid overfitting. Overfitting is the process of computing a predictive or classification model that describes random error, i.e., fits to the noise components of the observations, instead of the actual underlying relationships and salient features in the data.