Update project files

Author: rollingstorms

.github/workflows/pages.yml

@@ -1,43 +1,26 @@
-name: Deploy Docs to GitHub Pages
+name: Deploy Docs
 
 on:
   push:
     branches: [main]
   workflow_dispatch:
 
 permissions:
-  contents: read
-  pages: write
-  id-token: write
+  contents: write
 
 concurrency:
   group: "pages"
   cancel-in-progress: true
 
 jobs:
-  build:
+  deploy:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
         uses: actions/checkout@v4
 
-      - name: Setup Pages
-        uses: actions/configure-pages@v4
-        with:
-          enablement: true
-
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
-        with:
-          path: docs
-
-  deploy:
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-latest
-    needs: build
-    steps:
       - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: docs

README.md

@@ -6,6 +6,20 @@ Docs: https://rollingstorms.github.io/opproplot
 
 ![Opproplot hero](docs/assets/opproplot_hero.png)
 
+**What is an Operating Profile Plot?**
+
+An Operating Profile Plot (Opproplot) is a unified visualization for binary classifiers that shows how a model behaves across every possible decision threshold. It combines:
+- a stacked histogram of predicted scores for positives vs. negatives
+- threshold-dependent performance metrics such as Recall (TPR) and False Positive Rate (FPR)
+- computed at the midpoint of each score bin, treating each as a candidate threshold
+
+This creates a complete operating profile of the model in a single view — letting you see where the model is confident, where the classes overlap, and how performance changes as you move the threshold.
+
+Rather than switching between ROC curves, PR curves, histograms, and calibration plots, Opproplot places the score distribution and the operating characteristics on the same axis, making it easy to:
+- identify thresholds with optimal trade-offs
+- diagnose where errors occur in score space
+- communicate model quality in an interpretable, visual way
+
 **What it is:** Opproplot visualizes the family of decision rules h_t(x) = 1{f(x) >= t} and their induced operating characteristics (TPR, FPR, Accuracy), alongside the empirical score distributions p(s | Y=1) and p(s | Y=0).
 
 **Why it matters:** You see where positives and negatives sit in score space, how recall and false positives trade off at every cutoff, and where accuracy peaks—no context-switching between ROC curves, confusion matrices, and histograms.