ln vs log (#1161)

slds-lmu · Nov 9, 2023 · 2aa6024 · 2aa6024
1 parent d61d659
commit 2aa6024
Show file tree

Hide file tree

Showing 2 changed files with 1 addition and 1 deletion.
diff --git a/exercises-pdf/ic_classification_1.pdf b/exercises-pdf/ic_classification_1.pdf
diff --git a/exercises/supervised-classification/ex_rnw/ex_research-group-logreg.Rnw b/exercises/supervised-classification/ex_rnw/ex_research-group-logreg.Rnw
@@ -17,7 +17,7 @@ Researcher Lisa knows that logistic regression follows a discriminant approach,
 \end{align}
 Additionally, she recalls the Bernoulli loss function of the logistic regression model in statistics:
 \begin{align}
-\Lpixy = -\;y\ln(\pix)-(1-y)\ln(1-\pix)
+\Lpixy = \lcrossent %-\;y\ln(\pix)-(1-y)\ln(1-\pix)
 \end{align}
 Lastly, she recollects how logistic regression models the posterior probabilities $\pixt$ of the labels $\text{--}$ the estimated linear scores are "squashed" through the logistic function $s$:
 \begin{align}