Thesis: Added minor modification.

3_chapitre3.tex

@@ -145,7 +145,7 @@ These two baselines only use the feature set \emph{Rank}.
 In the following experiment is used the regression variants of Scikit-learn implementations of the Gradient Boosting Tree (\emph{GBR}), of the Support Vector Machine (\emph{SVR}) and of the \emph{ElasticNet} with their default hyperparameters, other values have been tested without significant performance change.
 Each model directly predicts the utility of the car in its corresponding (group of) cell(s) for the two previously mentioned feature sets.
 Each attribute had been normalized by subtracting the average and dividing by the standard deviation.
-Experiments were not done with other well-known machine learning models (such as Neural Networks or Random Forests) because of the small number of attributes available to describe the data and the presence of the mandatory \emph{car rank} attribute.
+Experiments were not done with other well-known machine learning models (such as Neural Networks or Random Forests) because of the small number of attributes available to describe the data, the small number of utility entries available and the presence of the mandatory \emph{car rank} attribute.
 Mandatory attributes are not suitable for the Random Forests algorithm.
 
 Other machine learning algorithms and learning strategies were tested but gave unconvincing results. 

5_chapitre5.tex

@@ -14,7 +14,9 @@ Lastly, the results of the A/B Testing are presented.
 \section{Experimental Setting}
 
 \textit{Free2Move} accepted to do an A/B Testing in order to assess the performance of the proposed methodology for a real case in \emph{Madrid}.
-The Figure~\ref{fig:ch5_madrid_area} shows the perimeter of the service with a blue line and all the hexagons used to make the grid representing the service's area.
+This service was selected over the services in \emph{Washington} and \emph{Paris} because experiments in Chapter~\ref{ch:method} shown both better performance in the prediction of the utility and a higher expected profit increase when using the method.
+Furthermore, \emph{Free2Move} is not responsible for the relocations in \emph{Washington}, the operator uses subcontractors, and in \emph{Paris} the operator's staff does not have room to make relocations.
+The Figure~\ref{fig:ch5_madrid_area} shows the perimeter of the service in \emph{Madrid} with a blue line and all the hexagons used to make the grid representing the service's area.
 Note, that it is not exactly the same perimeter as presented in Figure~\ref{fig:ch2_grid_madrid}, the one used during the experiments of Chapters~\ref{ch:method} and~\ref{ch:simulation}, which concern data about trips done four years earlier.
 
 \begin{figure}[!ht]

Makefile

@@ -1,7 +1,7 @@
 TARGET=main
 LATEX_FLAGS=--shell-escape
 
-all: pdf
+all: pdf clean
 	rsync -av . ~/Backup/Thesis/ --exclude .git
 
 # If there is no bibliography comment the bibtext dependency