Confusion matrix, Accuracy, Precision, Recall and F1

F1.ipynb

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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6\n",
      "Precision: 0.625\n",
      "Recall: 0.8333333333333334\n",
      "F1: 0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "import sklearn\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
    "\n",
    "class_names = ['A','B']\n",
    "y_test = [1,1,1,1,1,1,0,0,0,0]\n",
    "y_pred = [1,1,1,1,1,0,0,1,1,1]\n",
    "\n",
    "matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "\n",
    "# plot the matrix per se\n",
    "plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "    \n",
    "fmt = 'd'\n",
    "\n",
    "# write the number of predictions in each bucket\n",
    "thresh = matrix.max() / 2.\n",
    "for i, j in itertools.product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "    # if background is dark, use a white number, and vice-versa\n",
    "    plt.text(j, i, format(matrix[i, j], fmt),\n",
    "         horizontalalignment=\"center\",\n",
    "         color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "    \n",
    "tick_marks = np.arange(len(class_names))\n",
    "\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.ylabel('Expected',size=14)\n",
    "plt.xlabel('Predicted',size=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1:', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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