[add] Auto Format in GitHub actions

.github/workflows/WebPageToMarkdown.yml

@@ -29,6 +29,7 @@ jobs:
           ignoreSelector: '.ad-wrapper'
           markDownFilePath: './chinese/articles/'
           githubToken: ${{ github.token }}
+
       - name: Git Auto Commit
         uses: stefanzweifel/git-auto-commit-action@v5
         with:

.github/workflows/main.yml

@@ -1,19 +1,27 @@
 name: CI & CD
+
 on:
   push:
-    branches:
-      - main
+
 jobs:
-  Lint:
+  Lint-and-Format:
     runs-on: ubuntu-latest
     steps:
-      - name: Checkout
-        uses: actions/checkout@v3
-      - name: Use Node.js
-        uses: actions/setup-node@v3
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+        with:
+          version: 9
+      - uses: actions/setup-node@v4
         with:
           node-version: 'lts/*'
-      - name: Install & Lint
-        run: |
-          npm install
-          npm run lint
+          cache: pnpm
+      - name: Install Dependencies
+        run: pnpm i --frozen-lockfile
+
+      - name: Lint & Format
+        run: pnpm format
+
+      - uses: stefanzweifel/git-auto-commit-action@v5
+        with:
+          commit_message: Auto Format

.husky/pre-commit

@@ -1,5 +1 @@
-#!/usr/bin/env sh
-
-. "$(dirname -- "$0")/_/husky.sh"
-
 npm test

chinese/articles/machine-learning-handbook.md

@@ -1271,7 +1271,7 @@ Forward Step-Wise Selection can be summarized as follows:
 -   Consider all (p-k) models that contain the variables in M\_k with one additional feature or predictor.
 -   Choose the best model among these p-k models, and define it M\_(k+1) by using performance metrics such as [RSS][32]/[R-squared][33].
 
-****Step 3:**** Select the single model with the best performance among these M\_……….M\_p models (one with smallest [Cross Validation Error][34], [C\_p][35], [AIC][36] [(Akaike Information Criterion)][37], [BIC][38] (Bayesian Information Criteria)or [adjusted R-squared][39] is your best model M\*).
+****Step 3:**** Select the single model with the best performance among these M\…….M\_p models (one with smallest [Cross Validation Error][34], [C\_p][35], [AIC][36] [(Akaike Information Criterion)][37], [BIC][38] (Bayesian Information Criteria)or [adjusted R-squared][39] is your best model M\*).
 
 So, the idea behind this Selection is to start simple and increase the number of predictors in the model. Per number of predictors, consider all possible combination of variables and select a single best model: M\_k. Then compare all these models with different number of predictors (best M\_ks ) and the one best performing one can be selected.
 
@@ -1292,7 +1292,7 @@ Backward Step-Wise Feature Selection can be summarized as follows:
 -   Consider all k models that contain all variables except for one of the predictors in M\_k model, for k − 1 features.
 -   Choose the best model among these k models, and define it M\_(k-1) by using performance metrics such as [RSS][46]/[R-squared][47].
 
-****Step 3:**** Select the single model with the best performance among these M\_……….M\_p models (one with smallest [Cross Validation Error][48], [C\_p][49], [AIC][50][(Akaike Information Criterion)][51], [BIC][52] (Bayesian Information Criteria)or [adjusted R-squared][53] is your best model M\*).
+****Step 3:**** Select the single model with the best performance among these M\…….M\_p models (one with smallest [Cross Validation Error][48], [C\_p][49], [AIC][50][(Akaike Information Criterion)][51], [BIC][52] (Bayesian Information Criteria)or [adjusted R-squared][53] is your best model M\*).
 
 Like Forward Step-wise Selection, the Backward Step-Wise Feature Selection technique searches through only (p+1)/2 models, making it possible to apply in settings where p is too large to apply other selection techniques.
 

package.json

@@ -12,9 +12,9 @@
   },
   "devDependencies": {
     "@lint-md/cli": "^2.0.0",
-    "husky": "^8.0.3",
-    "lint-staged": "^13.3.0",
-    "prettier": "^3.0.3"
+    "husky": "^9.0.11",
+    "lint-staged": "^15.2.2",
+    "prettier": "^3.2.5"
   },
   "prettier": {
     "singleQuote": true,
@@ -28,6 +28,6 @@
     "lint": "lint-md chinese/**/*.md",
     "format": "lint-md chinese/**/*.md --fix",
     "test": "lint-staged",
-    "prepare": "husky install"
+    "prepare": "husky"
   }
 }