Posts Tagged ‘ computer ’

Classification from scratch, logistic with splines 2/8

May 30, 2018
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Classification from scratch, logistic with splines 2/8

Today, second post of our series on classification from scratch, following the brief introduction on the logistic regression. Piecewise linear splines To illustrate what’s going on, let us start with a “simple” regression (with only one explanatory variable). The underlying idea is natura non facit saltus, for “nature does not make jumps”, i.e. process governing equations for natural things are continuous. That seems to be a rather strong assumption, because…

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Classification from scratch, logistic regression 1/8

May 30, 2018
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Classification from scratch, logistic regression 1/8

Let us start today our series on classification from scratch… The logistic regression is based on the assumption that given covariates , has a Bernoulli distribution,The goal is to estimate parameter . Recall that the heuristics for the use of that function for the probability is that Maximimum of the (log)-likelihood function The log-likelihood is here where . Numerical techniques are based on (numerical) gradient descent to compute the maximum…

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Classification from scratch, overview 0/8

May 29, 2018
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Classification from scratch, overview 0/8

Before my course on « big data and economics » at the university of Barcelona in July, I wanted to upload a series of posts on classification techniques, to get an insight on machine learning tools. According to some common idea, machine learning algorithms are black boxes. I wanted to get back on that saying. First of all, isn’t it the case also for regression models, like generalized additive models (with splines)…

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On the interpretation of a regression model

May 18, 2018
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On the interpretation of a regression model

Yesterday, NaytaData (aka @NaytaData ) posted a nice graph on reddit, with bicycle traffic and mean air temperature, in Helsinki, Finland, per day, I found that graph interesting, so I did ask for the data (NaytaData kindly sent them to me tonight). df=read.csv("cyclistsTempHKI.csv") library(ggplot2) ggplot(df, aes(meanTemp, cyclists)) + geom_point() + geom_smooth(span = 0.3) But as mentioned by someone on twitter, the interpretation is somehow trivial : people get out on their…

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Justice “actuarielle”, algorithmes… et données

December 19, 2017
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Justice “actuarielle”, algorithmes… et données

Il y a un peu plus d’un an, Virginie Gautron m’envoyait plein de documents sur la “justice actuarielle”, concept que je découvrais alors. Pour comprendre un peu de quoi il s’agit, je peux renvoyer vers pénalité et gestion des risques : vers une justice « actuarielle » en Europe ? qui dresse un état de l’art, en francais. J’avoue avoir un peu mis de côté ensuite (faute de temps), et…

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The myth of interpretability of econometric models

December 9, 2017
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The myth of interpretability of econometric models

There are important discussions nowadays about data modeling, to choose between the “two cultures” (as mentioned in Breiman (2001)), i.e. either econometrics models or machine/statistical learning models. We did discuss this issue recently in Econométrie et Machine Learning (so far only in French) with Emmanuel Flachaire and Antoine Ly. One argument often used by econometricians is the interpretability of econometric models. Or at least the attempt to get an interpretable…

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A la recherche des homonymes…

October 6, 2017
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A la recherche des homonymes…

Il y a quelques mois, Baptiste Coulmont m’avait contacté avec une question passionnante (comme chaque fois qu’il me contacte). Baptiste voulait travailler sur la proportion de personnes qui ont un homonymes dans une population de taille donnée, ou sur la probabilité de ne pas avoir d’homonymes dans un bureau de vote, par exemple. Or ce dernier problème n’est pas sans rappeler le “paradoxe des anniversaires“. Dans un groupe de 23 personnes,…

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I Got The Feelin’

August 27, 2017
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I Got The Feelin’

Last week, I’ve been going through my CD collection, trying to find records I haven’t been listing for a while. And I got the feeling that music I listen to nowadays is slower than the one I was listening to in my 20’s. I was wondering if that was an age issue, or it was simply the fact that music in the 90s was “faster” than the one released in…

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Visualizing (censored) lifetime distributions

April 17, 2017
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Visualizing (censored) lifetime distributions

There are now more than 10,000 R packages available from CRAN, much more if you include those available only on github. So, to be honest, it become difficult to know all of them. But sometimes, you discover a nice function in one of them, and that is really awesome. Consider for instance some (standard) censored lifetime data, n=10000 idx=sample(1:4,size=n,replace=TRUE) pd=LETTERS[idx] lambda=1+(idx-1)/3 t=rexp(n,lambda) x=rexp(n) c=t>x y=pmin(t,x) df=data.frame(time=y,status=c,product=pd) (yes, I will generate…

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The U.S. Has Been At War 222 Out of 239 Years

March 19, 2017
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The U.S. Has Been At War 222 Out of 239 Years

This morning, I discovered an interesting statistic, America Has Been At War 93% of the Time – 222 Out of 239 Years – Since 1776,  i.e. the U.S. has only been at peace for less than 20 years total since its birth. I wanted to check, get a better understanding and look at other countries in the world. As always, we can try to extract information from wikipedia, since there are…

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