nbfmt

Author: tfdocsbot

site/en/tutorials/structured_data/imbalanced_data.ipynb

@@ -280,7 +280,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "96520cffee66"
+      },
       "outputs": [],
       "source": [
         "print(f'Average class probability in training set:   {train_labels.mean():.4f}')\n",
@@ -983,7 +985,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "52bd793e04bb"
+      },
       "outputs": [],
       "source": [
         "plot_cm(test_labels, test_predictions_baseline, threshold=0.1)\n",
@@ -1295,11 +1299,6 @@
         "\n",
         "Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {
@@ -1309,26 +1308,8 @@
       "toc_visible": true
     },
     "kernelspec": {
-      "display_name": "python3_general",
-      "language": "python",
+      "display_name": "Python 3",
       "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.15"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "dde43cc9f85737de16f122c427943bad6283df40a9f2bf91859955d42eb99337"
-      }
     }
   },
   "nbformat": 4,