Slide example to hold a presentation at the chair of statistics at HU Berlin.
\begin
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\usepackage{listings}
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\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{mathrsfs}
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\usepackage{mathtools}
\usepackage{algorithmic}
\usepackage[linesnumbered,ruled]{algorithm2e}
\usepackage{float}
% Change the pictures here:
% logobig and logosmall are the internal names for the pictures: do not modify them.
% Pictures must be supplied as JPEG, PNG or, to be preferred, PDF
\pgfdeclareimage[height=2cm]{logobig}{Figures/hulogo}
% Supply the correct logo for your class and change the file name to "logo". The logo will appear in the lower
% right corner:
\pgfdeclareimage[height=0.7cm]{logosmall}{Figures/hulogo}
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\renewcommand{\titlescale}{1.0}
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\renewcommand{\titlescale}{1.0}
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% smaller font for selected slides
\newcommand\Fontvi{\fontsize{10}{7.2}\selectfont}
\newcommand\Fontsm{\fontsize{8}{7.2}\selectfont}
% Define the title. Don't forget to insert an abbreviation instead
% of "title for footer". It will appear in the lower left corner:
\title[Title shown at each slide]{Title for title page}
% Define the authors:
\authora{Author 1} % a-c
\authorb{Author 2}
\authorc{Author 3}
% Define any internet addresses, if you want to display them on the title page:
\def\linka{http://lvb.wiwi.hu-berlin.de}
\def\linkb{www.case.hu-berlin.de}
\def\linkc{}
% Define the institute:
\institute{Ladislaus von Bortkiewicz Chair of Statistics \\
C.A.S.E. -- Center for Applied Statistics\\
and Economics\\
Humboldt--Universit{\"a}t zu Berlin \\}
% Comment the following command, if you don't want, that the pdf file starts in full screen mode:
\hypersetup{pdfpagemode=FullScreen}
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% Main document
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\begin{document}
% Draw title page
\frame[plain]{%
\titlepage{}
}
% The titles of the different sections of you talk, can be included via the \section command. The title will be displayed in the upper left corner. To indicate a new section, repeat the \section command with, of course, another section title
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\frame{
\frametitle{Outline}
\begin{enumerate}
\item Introduction
\item Pre-processing Steps
\item Model Selection
\item Variable Importance and Dimensionality Reduction
\item Results and Conclusion
\end{enumerate}
}
\section{Introduction}
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% (A numbering of the slides can be useful for corrections, especially if you are
% dealing with large tex-files)
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\frame{
\frametitle{Formal Problem Setting}
\begin{itemize}
\item \textit{training set}: inputs $X = (x_1,\dots,x_n) \in \mathbb{R}^{n \times d}$ and labels $Y = (y_1,\dots,y_n) \in \mathbb{R}^{n}$
\item \textit{test set}: inputs $X' = (x'_1,\dots,x'_t) \in \mathbb{R}^{t \times d}$ without labels
\end{itemize}
\vspace{0.5cm}
Find a function
\begin{align}
f: X\rightarrow Y
\end{align}
s.t. the \textit{test set} labels are predicted as accurately as possible, i.e.
\begin{align}
f(X') \approx Y'
\end{align}
}
\section{Pre-Processing}
\frame{
\frametitle{Outline}
\begin{enumerate}
\item Introduction \quad \checkmark
\item Pre-processing Steps
\item Model Selection
\item Variable Importance and Dimensionality Reduction
\item Results and Conclusion
\end{enumerate}
}
\frame{
\vspace{0.1cm}
Several transformations and cleaning steps needed before putting the data into an algorithm, e.g.
\frametitle{Pre-processing}
\begin{figure}
\begin{center}
\includegraphics[scale=0.25]{Figures/DataPipeline-1.jpg}
\caption{Workflow of Pre-Processing Steps}
\label{fig:DataPipeline}
\end{center}
\end{figure}
All transformation need to be preformed on the test set as well!
}
\begin{frame}[fragile]
\begin{center}
\begin{lstlisting}[
basicstyle=\tiny, %or \small or \footnotesize etc.
]
basic_preprocessing = function(X_com, y, scaler="gaussian")
{
source("replace_ratings.R")
source("convert_categoricals.R")
source("impute_data.R")
source("encode_time_variables.R")
source("impute_outliers.R")
source("scale_data.R")
source("delete_nearzero_variables.R")
X_ratings = replace_ratings(X_com)
X_imputed = naive_imputation(X_ratings)
X_no_outlier = data.frame(lapply(X_imputed, iqr_outlier))
X_time_encoded = include_quarter_dummies(X_no_outlier)
X_scaled = scale_data(X_time_encoded, scale_method = scaler)
X_encoded = data.frame(lapply(X_scaled, cat_to_dummy))
X_com = delect_nz_variable(X_encoded)
idx_train = c(1:length(y))
train = cbind(X_com[idx_train, ]
test = X_com[-idx_train, ]
return(list(train = train, X_com = X_com, test = test))
}
\end{lstlisting}
\end{center}
\quantnet \href{https://github.com/koehnden/SPL16/tree/master/Quantnet/dataProcessing/}{dataProcessing}
\end{frame}
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\section{Model Selection}
\frame{
\frametitle{Outline}
\begin{enumerate}
\item Introduction \quad \checkmark
\item Pre-processing Steps \quad \checkmark
\item Model Selection
\item Variable Importance and Dimensionality Reduction
\item Results and Conclusion
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]
\frametitle{Optimizing Hyper-parameters}
\begin{algorithm}[H]
\algsetup{linenosize=\tiny}
\scriptsize
\BlankLine
\ForEach{i in 1:t}
{
Randomly split the data into k folds of the same size \\
\ForEach{j in 1:k}
{
Use $j$th fold as test set and the union of remaining folds as training set \\
\ForEach{p in 1:grid}
{
Fit model on training set using parameter set $p$ \\
Predict on test set and calculate RMSE
}
}%end inner for
\ForEach{p in 1:grid}{
Calculate average RMSE over the $t \times k$-runs
}
choose $p$ with the lowest RMSE
}%end oute and r for
\caption{t-time k-fold crossvalidation and gridSearch}
\label{alg:seq}
\end{algorithm}
\quantnet \href{https://github.com/koehnden/SPL16/tree/master/Quantnet/xgbTuning/}{xgbTuning}
\quantnet \href{https://github.com/koehnden/SPL16/tree/master/Quantnet/rfTuning/}{rfTuning}
\quantnet \href{https://github.com/koehnden/SPL16/tree/master/Quantnet/svmTuning}{svmTuning}
\end{frame}
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\frame{
\frametitle{Taking on the curse of Dimensionality}
Problem:
\begin{itemize}
\item many variables (99 after pre-processing)
\item small training set ($n = 1460$)
\item variables are correlated with each other
\end{itemize}
\vspace{0.1cm}
Our approaches:
\begin{itemize}
\item Variable selection through variable importance ranking
\item Extract a smaller set of variable using PCA
\end{itemize}
}
\section{Results and Conclusion}
\frame{
\frametitle{Outline}
\begin{enumerate}
\item Introduction \quad \checkmark
\item Pre-processing Steps \quad \checkmark
\item Model Selection \quad \checkmark
\item Variable Importance and Dimensionality Reduction \quad \checkmark
\item Results and Conclusion
\end{enumerate}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\frame{
\frametitle{Results}
\begin{itemize}
\item Gaussian SVR with all variable is the single best model
\item PCA did not work well
\item Models perform best with the full set of variables as Figure \ref{fig:RFE} suggested
\end{itemize}
\vspace{0.25cm}
\begin{table}
\begin{center}
\begin{tabular}{c|ccc}
\hline\hline
Inputs & Gaussian SVR & Random Forest & GBM \\
\hline
All Variables & \textbf{0.1308} & 0.1484 & 0.1333 \\
Top 30 & 0.1323 & 0.1515 & 0.1436 \\
PCA & 0.1607 & 0.1657 & 0.1657 \\
\hline\hline
\end{tabular}
\caption{RMSE of submitted predictions}
\end{center}
\end{table}
\hspace{7.2cm} \href{https://github.com/koehnden/SPL16/blob/master/finalModels.R}{Github: finalModels}
}
\frame{
\frametitle{Outline}
\begin{enumerate}
\item Introduction \quad \checkmark
\item Pre-processing Steps \quad \checkmark
\item Model Selection \quad \checkmark
\item Variable Importance and Dimensionality Reduction \quad \checkmark
\item Results and Conclusion \quad \checkmark
\end{enumerate}
}
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% Dedicated section for references
\section{References}
\frame{
\frametitle{References}
\begin{thebibliography}{aaaaaaaaaaaaaaaaa}
\Fontvi
\beamertemplatearticlebibitems
\bibitem{Breiman:2003}
Breiman, Leo
\newblock{\em "Random Forest." Machine learning, 45(1), 5-32, (1999)}
\newblock available on \href{http://machinelearning202.pbworks.com/w/file/fetch/60606349/breiman_randomforests.pdf}{http://machinelearning202.pbworks.com}
\bibitem{ChenGuestrin:2015}
Chen, Tianqi, and Carlos Guestrin
\newblock{\em "XGBoost: Reliable Large-scale Tree Boosting System", Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining
Pages 785-794 (2015)}
\newblock available on \href{http://learningsys.org/papers/LearningSys_2015_paper_32.pdf}{http://learningsys.org}
\beamertemplatearticlebibitems
\bibitem{DeCock:2011}
De Cock, Dean
\newblock{\em "Ames, Iowa: Alternative to the Boston housing data as an end of semester regression project" Journal of Statistics Education 19.3 (2011)}
\newblock available on \href{https://ww2.amstat.org/publications/jse/v19n3/decock.pdf}{https://ww2.amstat.org}
\beamertemplatearticlebibitems
\end{thebibliography}
}
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\frame{
\frametitle{References}
\begin{thebibliography}{aaaaaaaaaaaaaaaaa}
\Fontvi
\beamertemplatearticlebibitems
\bibitem{Friedman:2003}
Friedman, Jerome H.
\newblock{\em "Greedy function approximation: a gradient boosting machine." Annals of statistics 1189-1232 (2001).}
\newblock available on \href{http://projecteuclid.org/download/pdf_1/euclid.aos/1013203451}{https://www.jstor.org/journal/annalsstatistics}
\bibitem{Kuhn:2015}
Kuhn, Max, and Kjell Johnson
\newblock{\em "Applied predictive modeling". New York: Springer (2013)}
\beamertemplatearticlebibitems
\bibitem{Vapnik:1997}
Vapnik, Vladimir, Steven E. Golowich, and Alex Smola
\newblock{\em "Support vector method for function approximation, regression estimation, and signal processing." Advances in neural information processing systems 281-287 (1997)}
\newblock available on \href{https://pdfs.semanticscholar.org/43ff/a2c1a06a76e58a333f2e7d0bd498b24365ca.pdf}{https://semanticscholar.org}
\beamertemplatearticlebibitems
\end{thebibliography}
}
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\end{document}