--- a
+++ b/Notebooks/17_confusion_matrix_test.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y     = torch.Tensor([[1,0,0,1,0], [0,0,0,1,1]]).numpy()\n",
+    "y_hat = torch.Tensor([[1,1,0,1,0], [1,1,0,0,1]]).numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.6)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat_binary = (y_hat > 0.5).astype(int)\n",
+    "accuracy = np.mean(y_hat_binary == y)\n",
+    "accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.])\n",
+      "tensor([1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 0., 1., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_true = torch.Tensor([[1,0,0,1,0,0,0], [0,0,0,1,1,0,0]]).flatten()\n",
+    "y_pred = torch.Tensor([[1,1,0,1,0,0,1], [1,1,0,0,1,0,0]]).flatten()\n",
+    "\n",
+    "print(y_true)\n",
+    "print(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 4 1 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
+    "print(tn, fp, fn, tp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6 4]\n",
+      " [1 3]]\n",
+      "tn fp\n",
+      "fn tp\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_true, y_pred))\n",
+    "print('tn fp\\nfn tp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "master",
+   "language": "python",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}