Publications by Mic

7th MilanoR meeting + talks live streaming

25.10.2016

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 first official R ev...

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Resizing spatial data in R

05.03.2017

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 data experience” less painful. I discovered that R is, as always, full of options.I’m used ...

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PCA revisited

12.08.2018

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 for now 😉I decided to write this post mainly for tw...

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PCA revisited: using principal components for classification of faces

13.08.2018

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 it.The goal of this post is to fit a simple classification model to predict...

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Linear programming in R

16.08.2018

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 $\hat X \geq 0$This doesn’t see...

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Linear programming in R

16.08.2018

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 $\hat X \geq 0$This doesn’t see...

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