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[Sap17b] 50 years of data analysis: from EDA to predictive modelling and machine learning
Conférences invitées : ASMDA 2017, June 2017, pp.3-4, Londres, UK,Mots clés: exploratory data analysis, machine learning, prediction
Résumé:
In 1962, J.W.Tukey wrote his famous paper “The future of data analysis†and promoted Exploratory Data Analysis (EDA), a set of simple techniques conceived to let the data speak, without prespecified generative models. In the same spirit J.P.Benzécri and many others developed multivariate descriptive analysis tools. Since that time, many generalizations occurred, but the basic methods (SVD, k-means, …) are still incredibly efficient in the Big Data era.
On the other hand, algorithmic modelling or machine learning are successful in predictive modelling, the goal being accuracy and not interpretability. Supervised learning proves in many applications that it is not necessary to understand, when one needs only predictions.
However, considering some failures and flaws, we advocate that a better understanding may improve prediction. Causal inference for Big Data is probably the challenge of the coming years.
Commentaires:
Applied Stochastic Models and Data Analysis
BibTeX
| @inproceedings { | |||
| Sap17b, | |||
| title | = | "{50 years of data analysis: from EDA to predictive modelling and machine learning }", | |
| author | = | " G. Saporta ", | |
| booktitle | = | "{ASMDA 2017}", | |
| year | = | 2017, | |
| month | = | "June", | |
| pages | = | "3-4", | |
| address | = | "Londres, UK", | |
| note | = | "{Applied Stochastic Models and Data Analysis }", | |
| } | |||
