On the type II error in SIMCA method

A novel method for theoretical calculation of the type II (β) error in soft independent modeling by class analogy is proposed. It can be used to compare tentatively predicted and empirically observed results of classification. Such an approach can better characterize model quality and thus improve its validation. Method efficiency is demonstrated on the famous Fisher Iris dataset and on a real‐world example of quality control of packed raw materials in pharmaceutical industry. Copyright © 2014 John Wiley & Sons, Ltd. A novel method for theoretical calculation of the type II (β) error in soft independent modeling by class analogy is proposed. It can be used to compare tentatively predicted and empirically observed results of classification. Such an approach can better characterize model quality and thus improve its validation. Method efficiency is demonstrated on the famous Fisher Iris dataset and on a real‐world example of quality control of packed raw materials in pharmaceutical industry