From 044f875728a732c0d3ae4ba0656b0aba63721446 Mon Sep 17 00:00:00 2001
From: AntoninDurousseau <antonin.durousseau@gmail.com>
Date: Tue, 3 Jun 2025 13:10:06 +0200
Subject: [PATCH] Change on cross validation

---
 knn.ipynb | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/knn.ipynb b/knn.ipynb
index 0bb1d45..1846ab8 100644
--- a/knn.ipynb
+++ b/knn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "id": "4e6f6cb1",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 36,
    "id": "4dd5223b",
    "metadata": {},
    "outputs": [],
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "id": "c1ab7ec9",
    "metadata": {},
    "outputs": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "id": "754dce9b",
    "metadata": {},
    "outputs": [
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 43,
    "id": "feb42adf",
    "metadata": {},
    "outputs": [
@@ -125,19 +125,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The best k for k-NN is k = 5\n"
+      "The best k for k-NN is k = 6\n"
      ]
     }
    ],
    "source": [
     "k_scores = []\n",
-    "K_list = np.arange(1, X.shape[0] // 4)  # concidering 1/4 of the samples as neaighbors is large enough for k-NN to don't overfit\n",
+    "K_list = np.arange(1, X.shape[0] // 4 )  # concidering 1/4 of the samples as neaighbors is large enough for k-NN to don't overfit\n",
     "\n",
     "for k in K_list:\n",
     "    knn = KNeighborsClassifier(n_neighbors=k)\n",
-    "    score_acc = cross_val_score(knn, X, y, cv=5, scoring='accuracy')\n",
-    "    score_f1 = cross_val_score(knn, X, y, cv=5, scoring='f1')\n",
-    "    score = (score_acc + score_f1) / 2 \n",
+    "    score = cross_val_score(knn, X, y, cv=5, scoring='accuracy')\n",
     "    k_scores.append(score.mean())\n",
     "\n",
     "k_scores = np.array(k_scores)\n",
@@ -147,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 40,
    "id": "fa8a7166",
    "metadata": {},
    "outputs": [