more updates for 2022

assignments/tex/assignment_1.tex

@@ -74,7 +74,7 @@ \section{Load and Explore Data.}
 
 \section{Train Interpretable Models}
 
-Train at least two types of interpretable models, ensuring best practices like reproducibility, validation-based early-stopping, and grid search are used. (Scikit-learn does not necessarily make applying such best practices easy.) You are encouraged to try packages like \href{https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html}{\texttt{h2o}}, \href{https://github.com/interpretml/interpret}{\texttt{interpret}}, and \href{https://xgboost.readthedocs.io/en/latest/install.html}{\texttt{XGBoost}}, but you may use any standard modeling approach, as long as it is interpretable and you will be able to apply explanation, discrimination testing and remediation, and model debugging approaches in coming weeks.\\
+Train at least two types of interpretable models, ensuring best practices like reproducibility, validation-based early-stopping, and grid search are used. (Scikit-learn does not necessarily make applying such best practices easy.) You are encouraged to try packages like \href{https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html}{\texttt{h2o}}, \href{https://github.com/interpretml/interpret}{\texttt{interpret}}, and \href{https://xgboost.readthedocs.io/en/latest/install.html}{\texttt{XGBoost}}, but you may use any interpretable or self-explainable approach on which you will be able to apply explanation, discrimination testing and remediation, and model debugging approaches in coming weeks. Please reach out with questions about appropriate modeling techniques.\\
 
 \noindent The template contains examples for elastic net logistic regression using \texttt{h2o} (see cells 8--10), monotonic gradient boosting machines (GBM) using \texttt{XGBoost} (see cells 12--14), and explainable boosting machines (EBM) using \texttt{interpret} (see cells 16--18). 
 
@@ -87,7 +87,7 @@ \section{Submit Code Results}
 	\item You must create submission files with output probabilities for each row of the test data. The submission file should have one column named \texttt{phat}. Each model should have a separate submission file named using a \texttt{<group\_indentifier>\_<model\_type>.csv} convention, similar to the \href{https://github.com/jphall663/GWU_rml/blob/master/assignments/assignment_1/ph_best_glm.csv}{example submission file}. See cells 11, 15, and 19 for examples of writing submission files. Your group's submission will be ranked using the cross-validated ranking method discussed during Lecture 1. The remaining five points for the assignment will be issued based on this ranking. You will have opportunities to increase your rank each week of the class. 
 \end{itemize}
 
-\noindent \textbf{Your deliverables are due Sunday, May 30\textsuperscript{th}, at 11:59:59 PM ET.}\\
+\noindent \textbf{Your deliverables are due Wednesday, May 25\textsuperscript{th}, at 11:59:59 PM ET.}\\
 
 \noindent Please send an email to \href{mailto:[email protected]}{\texttt{[email protected]}} by that deadline with the link to your group's GitHub page and with your zipped submission files. 
 

tex/lecture_1.tex

@@ -207,7 +207,7 @@
 
 			\vspace{10pt}
 
-			Self-explainable models are crucial for risk management, documentation, compliance, explanation of predictions to consumers, finding and fixing discrimination, debugging other problems in ML modeling pipelines. Simply put, \textbf{it is very difficult to mitigate risks you don't understand}.
+			Self-explainable models are crucial for risk management, documentation, compliance, explanation of predictions to consumers, finding and fixing discrimination, and  debugging other problems in ML modeling pipelines. Simply put, \textbf{it is very difficult to mitigate risks you don't understand}.
 
 			\vspace{10pt}
 
@@ -224,7 +224,7 @@
 			\small
 
 			\begin{itemize}
-				\item \textbf{Additivity}: Whether/how model takes the additive or modular form. Additive decomposition of feature effects tends to be more explainable.
+				\item \textbf{Additivity}: Whether/how model takes an additive or modular form. Additive decomposition of feature effects tends to be more explainable.
 				\item \textbf{Sparsity}: Whether/how features or model components are regularized. Having fewer features or components tends to be more explainable.
 				\item \textbf{Linearity}: Whether/how feature effects are linear. Linear or constant feature effects are easy to explain.
 				\item \textbf{Smoothness}: Whether/how feature effects are continuous and smooth. Continuous and smooth feature effects are relatively easy to explain.