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Update table settings 02 intrepretable models jakob #54

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Update table settings 02 intrepretable models jakob #54

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016d472
Update slides01-im-motivation.tex
jakobCampus Sep 1, 2023
c08c788
Update slides02-im-lm-simple.tex
jakobCampus Sep 1, 2023
1177284
Update slides03-im-lm-extensions.tex
jakobCampus Sep 1, 2023
80851fa
Update slides04-im-glm.tex
jakobCampus Sep 1, 2023
8d2a6cd
Update slides05-im-gam-boosting.tex
jakobCampus Sep 1, 2023
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Update slides06-im-rule-based.tex
jakobCampus Sep 1, 2023
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4 changes: 2 additions & 2 deletions slides/02_interpretable-models/slides01-im-motivation.tex
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\begin{frame}{Motivation}
%Achieving interpretability by using interpretable models is the most straightforward approach
%\bigskip
\begin{columns}[T, totalwidth = \linewidth]
\begin{columns}[T, totalwidth = \textwidth]
\begin{column}{0.55\textwidth}
\begin{itemize}
%\item Obtaining interpretations by using interpretable models is the easiest and least error-prone approach
Expand Down Expand Up @@ -105,7 +105,7 @@
\end{frame}

\begin{frame}{Disadvantages}
\begin{columns}
\begin{columns}[totalwidth=\textwidth]
\begin{column}{0.75\textwidth}
\begin{itemize}%[<+->]
\itemsep1em
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10 changes: 5 additions & 5 deletions slides/02_interpretable-models/slides02-im-lm-simple.tex
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\vspace{-0.2cm}
$$y = \theta_0 + \theta_1 x_1 + \theta_2 x_2 + \dots + \theta_p x_p + \epsilon = \xv^\top \thetab + \epsilon$$
\textbf{Feature importance}:
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.6\textwidth}
\begin{itemize}
\item Absolute \textbf{t-statistic} value: $\hat\theta_j$ scaled with standard error ($SE(\hat\theta_j)$ $\hat =$ reliability of estimate)
Expand All @@ -164,7 +164,7 @@
\end{columns}
\vspace{0.3cm}
\only<2->{
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.6\textwidth}
\begin{itemize}
\item \textbf{p-value}:
Expand Down Expand Up @@ -196,7 +196,7 @@
% \hat y = \hat \theta_0 + \hat \theta_1 \mathds{1}_{(seas = SPRING)} + \hat \theta_2 \mathds{1}_{(seas = SUMMER)} + \hat \theta_3 \mathds{1}_{(seas = FALL)} + \hat \theta_4 temp + \hat \theta_5 hum + \hat \theta_6 windspeed + \hat \theta_7 days\_since\_2011
% $$
% \end{footnotesize}
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.43\linewidth}
\vspace*{-0.3cm}
\begin{align*}
Expand Down Expand Up @@ -246,7 +246,7 @@
\pause

\lz
%\begin{columns}[T, totalwidth=\linewidth]
%\begin{columns}[T, totalwidth=\textwidth]
%\begin{column}<5>{0.46\textwidth}
%\textbf{Vis.}: Boxplot of $\hat\theta_j x_j$-values (scale invariant)\\
%weights multiplied by actual feature value (better comparable due to different scales)
Expand Down Expand Up @@ -277,4 +277,4 @@
\end{frame}

\endlecture
\end{document}
\end{document}
14 changes: 7 additions & 7 deletions slides/02_interpretable-models/slides03-im-lm-extensions.tex
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Equation above can be extended (polynomial regression) by including

\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.65\linewidth}
\begin{itemize}
\item \textbf{high-order effects} which have their own weights\\
Expand Down Expand Up @@ -115,7 +115,7 @@

\begin{frame}{Example: Interaction Effect}
\textbf{Example}: Interaction between \code{temp} and \code{season} will affect marginal effect of \code{temp}% with (right) and without (left) interaction.
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.65\linewidth}
\includegraphics[width = \textwidth]{figure/lm_main_vs_interaction_effects.pdf}
\end{column}
Expand Down Expand Up @@ -151,7 +151,7 @@
\end{columns}
\vfill
\pause
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.65\linewidth}
\textbf{Interpretation}: If \code{temp} increases by 1 $^{\circ}$C, bike rentals
\begin{itemize}[<+->]
Expand Down Expand Up @@ -180,7 +180,7 @@
\begin{column}{0.685\textwidth}
\includegraphics[width=0.5\textwidth, trim=0cm 0.1cm 10.4cm 0cm, clip]{figure/poly_main_vs_interaction_effects.pdf}\invisible<1>{\includegraphics[width=0.5\textwidth, trim=10cm 0.1cm 0.4cm 0cm, clip]{figure/poly_main_vs_interaction_effects.pdf}}
%\includegraphics[width = \textwidth]{figure/poly_main_vs_interaction_effects.pdf}
%\begin{columns}
%\begin{columns}[totalwidth=\textwidth]
%\begin{center}
%\adjincludegraphics[width=\textwidth, trim={0 0 {.55\width} 0}, clip]{figure/poly_main_vs_interaction_effects.pdf}

Expand Down Expand Up @@ -274,7 +274,7 @@
% % \item Penalization parameter $\lambda$ must be chosen (e.g., by CV) %or depending on the number of features to keep
% \end{itemize}

\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.6\textwidth}
\begin{itemize}
\item LASSO adds an $L_1$-norm penalization term ($\lambda||\theta||_1$) to least squares optimization problem
Expand Down Expand Up @@ -306,7 +306,7 @@
\item LASSO with main effects and interaction \code{temp} with \code{season}
\end{itemize}
\vspace{-0.5\topsep}
\begin{columns}[c, totalwidth=\linewidth]
\begin{columns}[c, totalwidth=\textwidth]
\begin{column}{0.65\textwidth}
\begin{itemize}
%\item LASSO with main effects and interaction \code{temp} with \code{season}
Expand Down Expand Up @@ -373,4 +373,4 @@
\end{frame}
\endlecture

\end{document}
\end{document}
6 changes: 3 additions & 3 deletions slides/02_interpretable-models/slides04-im-glm.tex
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$\leadsto$ Bernoulli / Binomial distribution
\end{itemize}
\vspace{-0.5cm}
\begin{columns}
\begin{columns}[totalwidth=\textwidth]
\begin{column}{0.45\textwidth}
\begin{itemize}
\item Target is count variable \\(e.g., number of sold products)\\
Expand Down Expand Up @@ -96,7 +96,7 @@
%\end{column}
%\begin{column}{0.55\textwidth}

% \begin{columns}[c, totalwidth=\linewidth]
% \begin{columns}[c, totalwidth=\textwidth]
% \begin{column}{0.005\linewidth}
% \scriptsize
% \rotatebox[origin=c]{90}{\hspace{10pt}$P(y = 1)$}
Expand Down Expand Up @@ -268,4 +268,4 @@
%------------------------------------------------------------------

\endlecture
\end{document}
\end{document}
14 changes: 7 additions & 7 deletions slides/02_interpretable-models/slides05-im-gam-boosting.tex
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\item<2-> Early stopping allows feature selection \& may prevent overfitting (regularization)
%\item Specifying loss and link function according to exponential family leads a (regularized) GLM
\end{itemize}
\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.49\linewidth}
\scriptsize
\begin{table}[ht]
Expand Down Expand Up @@ -233,7 +233,7 @@
\end{column}
\end{columns}

\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.49\linewidth}
\scriptsize
\centering
Expand Down Expand Up @@ -266,7 +266,7 @@
\end{itemize}
\medskip

\begin{columns}[T, totalwidth=\linewidth]
\begin{columns}[T, totalwidth=\textwidth]
\begin{column}{0.47\linewidth}
\hspace{46pt}{\scriptsize{1000 iterations}}
\includegraphics[width = \linewidth]{figure/compboost_pfi_base2.pdf}\\
Expand Down Expand Up @@ -305,7 +305,7 @@
\item BLs: linear and centered splines for numeric features, categorical for season
%and categorical base learner for season
\end{itemize}
\begin{columns}[T, totalwidth = \linewidth]
\begin{columns}[T, totalwidth = \textwidth]
\visible<2->{
\begin{column}{0.5\linewidth}
\hspace{45pt}{\scriptsize{Feature importance}}
Expand Down Expand Up @@ -343,7 +343,7 @@

% \begin{frame}{Decision Trees}

% \begin{columns}
% \begin{columns}[totalwidth=\textwidth]

% \begin{column}{0.3\textwidth}

Expand Down Expand Up @@ -486,7 +486,7 @@
% \item E.g., mean prediction for the first 105 days since 2011 is 1798 (applies to $\hat = 15\%$ of the data)
% \item \code{days\_since\_2011} shows highest feature importance (explains most of variance)
% \end{itemize}
% \begin{columns}[T]
% \begin{columns}[T, totalwidth=\textwidth]
% \begin{column}{0.4\textwidth}
% \vspace{1.5cm}
% \begin{table}[ht]
Expand Down Expand Up @@ -594,4 +594,4 @@


\endlecture
\end{document}
\end{document}
10 changes: 5 additions & 5 deletions slides/02_interpretable-models/slides06-im-rule-based.tex
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% \item<2-> Early stopping allows feature selection and might prevent overfitting (regularization)
% %\item Specifying loss and link function according to exponential family leads a (regularized) GLM
% \end{itemize}
% \begin{columns}[T]
% \begin{columns}[T, totalwidth=\textwidth]
% \begin{column}{0.49\textwidth}
% \scriptsize
% \begin{table}[ht]
Expand Down Expand Up @@ -322,7 +322,7 @@
% \item BLs: linear and centered splines for numeric features, categorical for season
% %and categorical base learner for season
% \end{itemize}
% \begin{columns}[T]
% \begin{columns}[T, totalwidth=\textwidth]
% \visible<2->{
% \begin{column}{0.5\textwidth}
% \hspace{45pt}{\scriptsize{Feature importance}}
Expand Down Expand Up @@ -393,7 +393,7 @@
% \medskip
% \scriptsize
% \pause
% \begin{columns}[T, totalwidth = \linewidth]
% \begin{columns}[T, totalwidth = \textwidth]
% \begin{column}{0.7\textwidth}
% \textbf{Example (selection bias)}:
% $n = 200,\, Y \sim N(0,1)$ \\
Expand Down Expand Up @@ -483,7 +483,7 @@

% \textbf{Example:} Bike data (here: linear model with temp in final nodes)

% \begin{columns}[T]
% \begin{columns}[T, totalwidth=\textwidth]
% \begin{column}{0.8\textwidth}
% \includegraphics[width = \textwidth]{figure/bike_mob.pdf}
% \end{column}
Expand Down Expand Up @@ -568,4 +568,4 @@
\end{columns}
\end{frame}
\endlecture
\end{document}
\end{document}