Agregate models with caretEnsemble

Introduction Suppose you have a dataset, and you are narowing possible machine learning models to 2 or 3 models, but you still cant choose which you want : Will the benefit of understandability from my CART cost me too much compare to a random forest or some bootsting ? Well you dont necessarily have to choose : juste agregate the models you have to make a better one. Typicaly, if you have models that dont uses the same features of the dataset, or give very different ansewrs but are still all good in term of a pre-selected metric (let’s say RMSE for regression, area under ROC for classification), ensembling them could be a good idea.

Around the Hull-White short-rate model

Après avoir rappelé la dynamique du taux court, sa solution dans le modèle Vasicek, ainsi quelques considérations à propos des processus d'Ornstein-Uhlenbeck, nous nous interessons au modèle à un facteur : le modèle de Hull et White. Nous en …