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z>OI@p)Q6!<7XanR+9M{)jtf;+Z1Fsk3}+K1`*aS7plnf~|I^N45+EKETdruFJMb!~6@KR|c(;s5{u diff --git a/tex/lecture_1.tex b/tex/lecture_1.tex index dd04105..45ceda6 100644 --- a/tex/lecture_1.tex +++ b/tex/lecture_1.tex @@ -262,7 +262,7 @@ \begin{itemize} \item{\textbf{Pearson correlation}: Measurement of the linear relationship between two input $X_j$ features; takes on values between -1 and +1, including 0.} - \item{\textbf{Shapley value}: a quantity, based in Game Theory, that accurately decomposes the outcomes of complex systems, like ML models, into individual components.} + \item{\textbf{Shapley value}: a quantity, based in game theory, that accurately decomposes the outcomes of complex systems, like ML models, into individual components.} \item{\textbf{Partial dependence and individual conditional expectation (ICE)}: Visualizations of the behavior of $X_j$ under some model $g$.} \end{itemize} @@ -399,7 +399,7 @@ \frametitle{Anatomy of Elastic Net Regression} - Generalized linear models (GLM) have the same basic functional form as more traditional linear models, e.g. ... + Penalized linear models have the same basic functional form as more traditional linear models, e.g. ... \begin{equation} \begin{aligned}\label{eq:glm2} @@ -463,9 +463,9 @@ GAMs use spline approaches to fit each $g_j$.\\ \vspace{10pt} - Later \cite{ga2m} introduced an efficient technique for finding interaction terms ($\beta_{j,k} g_{(j-1),(k-1)}(x_{j-1}, x_{k-1})$) to include in GAMs. This highly accurate technique was given the acronym GA2M.\\ + Later \cite{ga2m} introduced an efficient technique for finding interaction terms ($\beta_{j,k} g_{j,k}(x_j, x_k)$) to include in GAMs. This highly accurate technique was given the acronym GA2M.\\ \vspace{10pt} - Recently Microsoft Research introduced the explainable boosting machine (EBM) in the \href{https://github.com/interpretml/interpret/}{interpret} package, in which GBMs are used to fit each $g_{j-1}$ and $g_{(j-1),(k-1)}$. Higher order interactions are allowed, but used infrequently in practice. \\ + Recently Microsoft Research introduced the explainable boosting machine (EBM) in the \href{https://github.com/interpretml/interpret/}{interpret} package, in which GBMs are used to fit each $g_{j}$ and $g_{j,k}$. Higher order interactions are allowed, but used infrequently in practice. \\ \vspace{10pt} Because each input feature, or combination thereof, is treated separately and in an additive fashion, explainability is very high. @@ -488,7 +488,7 @@ \frametitle{Generalized Additive Models and Neural Networks} - \noindent Researchers have also put forward GA2M variants in which each $g_{j-1}$ and $g_{(j-1),(k-1)}$ shape function is fit by neural networks, e.g., GAMI-Net (\citet{yang2021gami}) and neural additive models (\citet{agarwal2021neural}).\\ + \noindent Researchers have also put forward GA2M variants in which each $g_{j}$ and $g_{j, k}$ shape function is fit by neural networks, e.g., GAMI-Net (\citet{yang2021gami}) and neural additive models (\citet{agarwal2021neural}).\\ \vspace{10pt} \noindent See the \href{https://selfexplainml.github.io/PiML-Toolbox/_build/html/index.html}{PiML package} for an excellent implementation of GAMI-Net and other explainable models. @@ -587,6 +587,7 @@ \begin{itemize} \item Generally speaking, standard ML evaluation -- including Kaggle leaderboards, are poor ways to assess ML model performance. \item However, \cite{caruana2004kdd} puts forward a robust model evaluation and selection technique based on cross-validation and ranking. + \item PiML contains excellent real-world model validation approaches as well. \end{itemize} \column{0.6\linewidth}