Updated visualization

ml2/3_7_SVM.ipynb

@@ -535,13 +535,13 @@
    "source": [
     "# This step will take some time\n",
     "# Cross-validationt\n",
-    "cv = KFold(n_splits=5, shuffle=False, random_state=33)\n",
+    "cv = KFold(n_splits=5, shuffle=True, random_state=33)\n",
     "# StratifiedKFold has is a variation of k-fold which returns stratified folds:\n",
     "# each set contains approximately the same percentage of samples of each target class as the complete set.\n",
-    "#cv = StratifiedKFold(y, n_folds=3, shuffle=False, random_state=33)\n",
+    "#cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=33)\n",
     "scores = cross_val_score(model, X, y, cv=cv)\n",
     "print(\"Scores in every iteration\", scores)\n",
-    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n"
+    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
    ]
   },
   {
@@ -644,7 +644,7 @@
    "source": [
     "* [Titanic Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
     "* [API SVC scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)\n",
-    "* [Better evaluation of classification models](http://blog.kaggle.com/2015/10/23/scikit-learn-video-9-better-evaluation-of-classification-models/)"
+    "* [How to choose the right metric for evaluating an ML model](https://www.kaggle.com/vipulgandhi/how-to-choose-right-metric-for-evaluating-ml-model)"
    ]
   },
   {
@@ -666,7 +666,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -680,7 +680,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,