Tag Archives: ML

SCOR Foundation for Science Webinar, ML and Econometrics

This week, I will give a talk at the SCOR Foundation for Science webinar (slides are available online). and I was asked to give a talk on econometrics vs IA (or machine learning),

Of course, the two concepts are related, and there is a continuum between them.

As we wrote it Charpentier et al. (2017)

Econometrics and machine learning seem to have one common goal: to construct a predictive model, for a variable of interest, using explanatory variables (or features).

For the purposes of this presentation, we will begin by contrasting the two, emphasizing the differences, and then showing the connections that exist.

Long story short, in between, we have computation statistics, or statistical learning, corresponding to computational techniques with mathematical probabilistic guarantees.

Continue reading SCOR Foundation for Science Webinar, ML and Econometrics

Conference in Montpellier (France), on calibration

This morning, I will present at the “quatrième Journée d’Econometrie appliquée, en l’honneur de Michel Terraza”. I will present recent work with Agathe Fernandes Machado, Ewen Gallic, François Hu, and Emmanuel Flachaire. Slides are available. The talk is on “Calibration, ou interprétation probabiliste des scores de modèles boites noires” (Calibration, or probabilistic interpretationof black box model scores, but slides are in English).

Machine Learning in Actuarial Science and Insurance

This week is organized the summer school on machine learning for economists and applied social scientists. I will be giving an (online) lecture this Thursday, on Machine Learning in Actuarial Science & Insurance, with a great program,

  • 10am – 10:55am : Juri Marcucci: Machine Learning in Macroeconomics
  • 11am – 11:55am : Arthur Charpentier: Machine Learning in Actuarial Science & Insurance
  • 12pm – 12:55pm : Arthur Spirling : Machine Learning in Embeddings Representations
  • 1pm – 1:55pm : Kathy Baylis: Machine Learning in Agricultural Economics
  • 2pm – 2:55pm : Stefan Wager : Machine Learning in Causal Inference
  • 10am – 10:55am : Stan Matwin : Machine Learning and Data Privacy
  • 11am – 11:55am : Mehmet Caner : Machine Learning in Econometrics
  • 12pm – 12:55pm : Anders Bredahl Kock : Machine Learning in Model Selection
  • 1pm – 1:55pm : Dario Sansone: Machine Learning in Education and Development Economics
  • 2pm – 2:55pm: Patrick Baylis :Temperature and Temperament: Evidence from Twitter

My slides are now online,

Quelles limites pour les algorithmes d’apprentissage ?

Avec Michel Denuit, on vient de publier un court article, quelles limites pour les algorithmes d’apprentissage ? Une version en anglais devrait paraître dans un ouvrage collectif dans les semaines à venir.

Une étape essentielle du processus de tarification est le choix de l’algorithme qui permettra de calculer la prime à partir de données passées. En assurance, les modèles prédictifs sont partout : il peut s’agir de calculer la prime demandée pour couvrir les dommages causés à un bien, ou encore d’un score interne de suspicion de fraude sur un sinistre. Ce papier discute des limites de l’application des algorithmes prédictifs en assurance en revenant sur les notions de segmentation des risques et d’hétérogénéité.