Model deployment with Sipina

Model deployment is the last step of the Data Mining process. In its simplest form in a supervised learning task, it consists in to apply a predictive model on unlabeled cases.

Applying the model on unseen cases is a very useful functionality. But it would be even more interesting if we could announce its accuracy. Indeed, a misclassification can have dramatic consequences. We must measure the risk we take when we make decisions from a predictive model. An indication about the performance of a classifier is important when we decide or not to deploy it.

In this tutorial, we show how to apply a classifier on unlabeled sample with Sipina. We show also how to estimate the generalization error rate using a resampling scheme such as bootstrap.

Keywords: model deployment, unseen cases, unlabeled instances, decision tree, sipina, linear discriminant analysis

Tutorial: en_sipina_deployment.pdf

Dataset: wine_deployment.xls

References:

Tanagra Tutorials, "Applying a classifier on a new dataset (Deployment)"