Articles by Mic

Linear programming in R

August 16, 2018 | Mic

Linear programming is a technique to solve optimization problems whose constraints and outcome are represented by linear relationships. Simply put, linear programming allows to solve problems of the following kind: Maximize/minimize $\hat C^T \hat X$ Under the constraint $\hat A \hat X \leq \hat B$ And the constraint $\...

Linear programming in R

August 16, 2018 | Mic

Linear programming is a technique to solve optimization problems whose constraints and outcome are represented by linear relationships. Simply put, linear programming allows to solve problems of the following kind: Maximize/minimize $\hat C^T \hat X$ Under the constraint $\hat A \hat X \leq \hat B$ And the constraint $\...

PCA revisited: using principal components for classification of faces

August 13, 2018 | Mic

This is a short post following the previous one (PCA revisited). In this post I’m going to apply PCA to a toy problem: the classification of faces. Again I’ll be working on the Olivetti faces dataset. Please visit the previous post PCA revisited to read how to download ...

PCA revisited

August 12, 2018 | Mic

Principal component analysis (PCA) is a dimensionality reduction technique which might come handy when building a predictive model or in the exploratory phase of your data analysis. It is often the case that when it is most handy you might have forgot it exists but let’s neglect this aspect ...

Resizing spatial data in R

March 5, 2017 | Mic

Here I am after a short break, writing again about R!In december I worked on a project that required me to work on spatial data. This led me to learn about how R deals with this kind of data and to look around for ways to make my “spatial ...

7th MilanoR meeting + talks live streaming

October 25, 2016 | Mic

On 27th of October I’m going to attend the 7th MilanoR meeting featuring the following two talks: 1. Interactive big data analysis with R: SparkR and MongoDB: a friendly walkthrough by Thimoty Barbieri and Marco Biglieri 2. Power consumption prediction based on statistical learning techniques by Davide Pandini This is my ... [Read more...]

My first Shiny App: control charts

September 24, 2016 | Mic

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 tha...

Image recognition tutorial in R using deep convolutional neural networks (MXNet package)

August 5, 2016 | Mic

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 ...

Plain vanilla recurrent neural networks in R: waves prediction

August 5, 2016 | Mic

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 w...

Image recognition in R using convolutional neural networks with the MXNet package

July 25, 2016 | Mic

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 f...

Image recognition in R using convolutional neural networks with the MXNet package

July 25, 2016 | Mic

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 f...

How to fit a copula model in R [heavily revised]. Part 2: fitting the copula

March 29, 2016 | Mic

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 ...

How to fit a copula model in R [heavily revised]. Part 1: basic tools

March 13, 2016 | Mic

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 ...

Selecting the number of neurons in the hidden layer of a neural network

September 28, 2015 | Mic

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 ... [Read more...]

Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

September 15, 2015 | Mic

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 u... [Read more...]

Predicting creditability using logistic regression in R (part 1)

September 2, 2015 | Mic

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.... [Read more...]

Simple regression models in R

August 1, 2015 | Mic

Linear regression models are one the simplest and yet a very powerful models you can use in R to fit observed data and try to predict quantitative phenomena. Say you know that a certain variable y is somewhat correlated with a certain variable x and you can reasonably get an ...

Estimating arrival times of people in a shop using R

July 30, 2015 | Mic

Since the first time I was taught the Poisson and Exponential distributions I have been wondering how to apply them to model a real world scenario such as arrivals of people in a shop. Last week I was in a big shopping center and I thought that was the perfect ...

Estimating data parameters using R

July 11, 2015 | Mic

Say we have some data and we are pretty confident that it comes from a random variable which follows a Normal distribution, now we would like to estimate the parameters of that distribution. Since the best estimator for the population mean is the sampl...

How to make a rough check to see if your data is normally distributed

July 11, 2015 | Mic

Now then, in the previous article I wrote about hypothesis testing with data that is normally distributed, in this article I’m going to post some quick test you can do to check if it is fairly safe to assume your data is normal. I would like to highlight the ... [Read more...]

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