Comments

pres.tex

@@ -186,7 +186,7 @@
     $$\xi(x) = \operatornamewithlimits{argmin}_{g \in G} \mathcal{L}(f,g,\pi_x) + \Omega(g)$$
   \end{frame}
   \begin{frame}
-    % Note: WTF is x' here? Should it not just be x?
+    % Note: WTF is x' here? - An interpretable version of x
     % Note: g acts in d' while f acts in d, so when we say that we have z' in dimension d', it's the model g, we can recover the z in the original representation i.e. explained by f in dimension d.
     \frametitle{Sampling for Local Exploration}
     Goal: Minimizing $\mathcal{L}(f,g,\pi_x)$ without making assumptions on $f$
@@ -217,7 +217,7 @@
   
   \begin{frame}
     % \frametitle{Sparse Linear Explanations}
-    % Talk through the algorithm, discussing the sampling and K-Lasso
+    % Talk through the algorithm, discussing the sampling and K-Lasso (least absolute shrinkage and selection operator), which is used for feature selection
     \frametitle{Explaining an individual prediction}
     \begin{algorithm}[H]
       \setstretch{0.9}
@@ -236,7 +236,7 @@
  \caption{Sparse Linear Explanations using LIME}
 \end{algorithm}
 % This algorithm approximates the minimization problem of computing a single individual explanation of a prediction.
-% K-Lasso is the procedure of learning the weights via least squares. Wtf are these weights???
+% K-Lasso is the procedure of learning the weights via least squares. Wtf are these weights??? - The features
   \end{frame}
 
   
@@ -254,7 +254,7 @@
     Idea: We give a global understanding of the model by explaining a set of individual instances
     \begin{itemize}
     \item Still model agnositc (since the indiviudal explanations are)
-    \item Instances need to be selected in a clever way
+    \item Instances need to be selected in a clever way, as people won't have time to look through all explanations
     \item Some definitions
       \begin{itemize}
       \item Time/patience of humans is explained by a budget \emph{B} which denotes number of explanations a human will sit through.
@@ -300,6 +300,8 @@
     \frametitle{Definitions}
     \begin{itemize}
     \item $I_j = \sqrt{\sum_{i=1}^n W_{ij}}$
+      % c is a coverage function, which computes the total importance of the features that appear in at least one instance in a set V .
+      % NOte: maximizing a weighted coverage function is NP-hard, but the version used in the algorithm is iterativily greedy, so it just adds the one with the maximum gain, which offers a constant-factor approximation guarantee of 1−1/e to the optimum.
     \item $c(V,W,I) = \sum\limits_{j=1}^{d^\prime} \mathbbm{1}_{[\exists i \in V : W_{ij} > 0]}\ I_j$
       \item $Pick(W,I) = \operatornamewithlimits{argmax}\limits_{V,|V| \leq B} c(V,W,I)$
     \end{itemize}
@@ -373,6 +375,16 @@
 
   \begin{frame}
     \frametitle{Faithfulness}
+    \begin{itemize}
+    \item Faithfulness of explanations is measured on classifiers that are interpretable, \textbf{LR} and \textbf{DT}. Both are trained s.t. the max no. of features is $10$, so features found by these are the \emph{gold standard} of features, in regards to which are important.
+      \item For each prediction on the test set, explanations are produced and the fraction of the gold features found, is computed.
+    \end{itemize}
+  \end{frame}
+  
+  \begin{frame}
+    \frametitle{Faithfulness}
+    % We observe that the greedy approach is comparable to parzen on logistic regression, but is substantially worse on decision trees since changing a single feature at a time often does not have an effect on the prediction. The overall recall by parzen is low, likely due to the difficulty in approximating the original highdimensional classifier. LIME consistently provides > 90% recall for both classifiers on both datasets, demonstrating that LIME explanations are faithful to the models.
+
     \centering
     % Books faithfulness
     \includegraphics[height=0.35\textheight]{graphics/books_dt_lr.png}{ }
@@ -382,11 +394,8 @@
   \end{frame}
   
   \begin{frame}
-    \frametitle{Can users select the best classifier?}
-    \begin{itemize}
-    \item Faithfulness of explanations is measured on classifiers that are interpretable, \textbf{LR} and \textbf{DT}. Both are trained s.t. the max no. of features is $10$, so features found by these are the \emph{gold standard} of features, in regards to which are important.
-      \item For each prediction on the test set, explanations are produced and the fraction of the gold features found, is computed.
-    \end{itemize}
+    \frametitle{Simulated human shenanigans}
+    % In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive). The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. 
   \end{frame}
 
   \begin{frame}