Publications by Mic
Predicting creditability using logistic regression in R (part 1)
As I said in the previous post, this summer I’ve been learning some of the most popular machine learning algorithms and trying to apply what I’ve learned to real world scenarios. The German Credit dataset provided by the UCI Machine Learning Repository is another great example of application.The German Credit dataset contains 1000...
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Predicting creditability using logistic regression in R: cross validating the classifier (part 2)
Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the ...
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Selecting the number of neurons in the hidden layer of a neural network
Recently I wrote a post for DataScience+ (which by the way is a great website for learning about R) explaining how to fit a neural network in R using the neuralnet package, however I glossed over the “how to choose the number of neurons in the hidden layer” part. The glossing over is mainly due to the fact that there is no fixed rule or sugge...
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How to fit a copula model in R [heavily revised]. Part 1: basic tools
More than a year ago I wrote a short post on how to fit a copula model in R. The post showed how to make a very raw and basic fitting of a test dataset to a two dimensional normal copula (or a gaussian copula if you wish) using the copula package. The content of the post was not much, but that was a big part of what I knew about how to use copula...
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How to fit a copula model in R [heavily revised]. Part 2: fitting the copula
Here I am again with part 2. If you would like to read part 1 of this short tutorial on copulas, please click here. In this second post I am going to select a copula model, fit it to a test dataset, evaluate the fitting and generate random observations from the fitted multivariate distribution. Furthermore I am going to show how to measure correl...
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Image recognition in R using convolutional neural networks with the MXNet package
Among R deep learning packages, MXNet is my favourite one. Why you may ask? Well I can’t really say why this time. It feels relatively simple, maybe because at first sight its workflow looks similar to the one used by Keras, maybe because it was my first package for deep learning in R or maybe because it works very good with little effort, who ...
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Image recognition in R using convolutional neural networks with the MXNet package
Among R deep learning packages, MXNet is my favourite one. Why you may ask? Well I can’t really say why this time. It feels relatively simple, maybe because at first sight its workflow looks similar to the one used by Keras, maybe because it was my first package for deep learning in R or maybe because it works very good with little effort, who ...
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Plain vanilla recurrent neural networks in R: waves prediction
While continuing my study of neural networks and deep learning, I inevitably meet up with recurrent neural networks. Recurrent neural networks (RNN) are a particular kind of neural networks usually very good at predicting sequences due to their inner working. If your task is to predict a sequence or a periodic signal, then using a RNN might be a ...
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Image recognition tutorial in R using deep convolutional neural networks (MXNet package)
This is a detailed tutorial on image recognition in R using a deep convolutional neural network provided by the MXNet package. After a short post I wrote some times ago I received a lot of requests and emails for a much more detailed explanation, therefore I decided to write this tutorial. This post will show a reproducible example on how to get ...
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My first Shiny App: control charts
After having carefully followed the online official Shiny tutorial, I decided to make a quick try at making my very first Shiny App. I should say that I found myself very well with the explanation given and Shiny was definitely one of the libraries that took the less time for me to start using it. Of course I’m still not a master of Shiny by no...
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