machine_learning/muticlass_logistic_regression_8/multiclass_logitstic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31637f6b",
   "metadata": {},
   "source": [
    "## 1.手写体识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bab32d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80edf133",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ea44c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d36f2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 1797\\n    :Number of Attributes: 64\\n    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n    :Missing Attribute Values: None\\n    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n    :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n    Graduate Studies in Science and Engineering, Bogazici University.\\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n    Linear dimensionalityreduction using relevance weighted LDA. School of\\n    Electrical and Electronic Engineering Nanyang Technological University.\\n    2005.\\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\\n    Algorithm. NIPS. 2000.\\n\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.DESCR#discription"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "938ce0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14cac6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
       "        [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
       "        [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
       "        [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
       "        [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
       "        [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
       "        [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
       "        [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]]),\n",
       " 0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images[0], digits.target[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85db1c61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3c67f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2da27e566c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()#如果不加gray，即使是灰度图，plt默认三个通道都相同的值，所以显示有颜色。\n",
    "plt.matshow(digits.images[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501e6e01",
   "metadata": {},
   "source": [
    "显示多个。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5494019a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "fig = plt.figure()\n",
    "for i, (images, labels) in enumerate(zip(digits.images[:9], digits.target[:9])):\n",
    "    fig.add_subplot(3,3,i+1)\n",
    "    plt.title(str(labels))\n",
    "    plt.imshow(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e00abaa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2e5c6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9af25e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2c62100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 1437, 360)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(digits.data), len(x_train), len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "558522cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc3d8214",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d3dfb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\envs\\stark-lin\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ffce7229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb88e9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2da2b608f88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(digits.images[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "829fe818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61422431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([digits.data[100]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43d3dd43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(digits.data[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38511984",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4a66067",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "319a768d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f878c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'True')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwwAAAK9CAYAAACJnusfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABzZUlEQVR4nO3dd3hU5db38d9AkiEkIQiplFCCNDEg1UhHBFERBbGhNMVGR/QYC0XUUFRApahIUUEQPChwBESqPIKGIEVFIEiRGpBDAoHU2e8fvuTMCAMEM7OH7O/nufZ1ntlT7rVyJzGLdd972wzDMAQAAAAAF1HM7AAAAAAA+C4KBgAAAABuUTAAAAAAcIuCAQAAAIBbFAwAAAAA3KJgAAAAAOAWBQMAAAAAtygYAAAAALhFwQAAAADALQoGANecESNGyGazXdV7Z86cKZvNpn379hVuUFDlypXVs2fPK3ptq1at1KpVK4/GAwAoHBQMAEx1/g/480eJEiVUrlw5tW/fXu+8845Onz7t8RgmT56smTNnFtrnrVmzxiWn4sWLKyIiQvfdd5927NhRaOP4ul9//VUjRoygOAOAa5zNMAzD7CAAWNfMmTPVq1cvvfrqq6pSpYpycnJ09OhRrVmzRitWrFBMTIwWLVqkuLi4/Pfk5uYqNzdXJUqUKPB4eXl5ysnJkd1uz+9S1KlTR2FhYVqzZk2h5LRmzRq1bt1aAwYMUKNGjZSTk6Nt27Zp6tSpCgoK0s8//6yoqKhCGcuXZGVlqVixYvL395ckLViwQF27dtXq1asv6CZkZ2dLkgICArwdJgCggPzMDgAAJKlDhw5q2LBh/uOEhAStWrVKd911l+6++27t2LFDgYGBkiQ/Pz/5+V3dr6/ixYurePHihRLz5TRv3lz33Xdf/uMaNWro6aef1scff6znn3/eKzF4k91uv+LXUigAwLWDJUkAfFabNm30yiuvaP/+/fr000/zz19sD8O5c+c0YMAAhYWFKSQkRHfffbcOHTokm82mESNG5L/u73sYKleurF9++UVr167NX0J0/l/Dc3JyNHLkSF1//fUqUaKEypYtq2bNmmnFihVXlU/z5s0lSXv27HE5f+jQIfXu3VuRkZGy2+264YYbNH369Aven5mZqREjRqh69eoqUaKEoqOj1blzZ5fPy8jI0LPPPquKFSvKbrerRo0aevPNN/X3ZvKVfr3Of61TUlLUs2dPlS5dWqGhoerVq5fOnj3r8pnOexhmzpyprl27SpJat26d/7U938W52B6G1NRUPfbYY4qMjFSJEiVUt25dzZo1y+U1+/btk81m05tvvqkPPvhAsbGxstvtatSokZKSki49AQCAq0KHAYBPe/TRR/Xiiy/qm2++UZ8+fdy+rmfPnvr888/16KOP6uabb9batWt15513XvbzJ0yYoP79+ys4OFgvvfSSJCkyMlLSX38sJyYm6vHHH1fjxo2Vnp6uTZs2afPmzbrtttsKnMv5IuW6667LP3fs2DHdfPPNstls6tevn8LDw7V06VI99thjSk9P16BBgyT9tZTqrrvu0sqVK/Xggw9q4MCBOn36tFasWKGff/5ZsbGxMgxDd999t1avXq3HHntM9erV0/Lly/Xcc8/p0KFDGj9+/FV/ve6//35VqVJFiYmJ2rx5s6ZNm6aIiAiNGTPmoq9v0aKFBgwYoHfeeUcvvviiatWqJUn5//t3586dU6tWrZSSkqJ+/fqpSpUqmj9/vnr27KlTp05p4MCBLq+fM2eOTp8+rSeffFI2m01jx45V586d9fvvv+cviQIAFBIDAEw0Y8YMQ5KRlJTk9jWhoaHGTTfdlP94+PDhhvOvr+TkZEOSMWjQIJf39ezZ05BkDB8+/ILx9u7dm3/uhhtuMFq2bHnBuHXr1jXuvPPOAue0evVqQ5Ixffp04/jx48bhw4eNZcuWGdWqVTNsNpvx448/5r/2scceM6Kjo40TJ064fMaDDz5ohIaGGmfPnjUMwzCmT59uSDLefvvtC8ZzOByGYRjGl19+aUgyXnvtNZfn77vvPsNmsxkpKSmGYRTs63X+a927d2+X1957771G2bJlXc5VqlTJ6NGjR/7j+fPnG5KM1atXXxBzy5YtXb7mEyZMMCQZn376af657OxsIz4+3ggODjbS09MNwzCMvXv3GpKMsmXLGidPnsx/7VdffWVIMhYvXnzBWACAf4YlSQB8XnBw8CWvlrRs2TJJ0jPPPONyvn///v9o3NKlS+uXX37R7t27r+r9vXv3Vnh4uMqVK6fbb79daWlp+uSTT9SoUSNJkmEY+uKLL9SxY0cZhqETJ07kH+3bt1daWpo2b94sSfriiy8UFhZ20ZzOL8/6+uuvVbx4cQ0YMMDl+WeffVaGYWjp0qWSru7r9dRTT7k8bt68uf7880+lp6cX5Evi1tdff62oqCg99NBD+ef8/f01YMAAnTlzRmvXrnV5/QMPPODSqTm/3Ov3338vlHgAAP9DwQDA5505c0YhISFun9+/f7+KFSumKlWquJyvVq3aPxr31Vdf1alTp1S9enXdeOONeu6557Rt27Yrfv+wYcO0YsUKLVy4UN27d1daWpqKFfvfr93jx4/r1KlT+uCDDxQeHu5y9OrVS9Jf6/qlv/Y91KhR45Kbvffv369y5cpd8LU6vwxo//79+f9b0K9XTEyMy+Pzf6z/97//veTX4Ert379f119/vcvXR7owdm/FAwD4H/YwAPBpBw8eVFpa2j/+4/9qtGjRQnv27NFXX32lb775RtOmTdP48eM1depUPf7445d9/4033qi2bdtKku655x6dPXtWffr0UbNmzVSxYkU5HA5J0iOPPKIePXpc9DOcLydrJndXljJMujK3r8UDAEUZHQYAPu2TTz6RJLVv397taypVqiSHw6G9e/e6nE9JSbmiMS511+gyZcqoV69e+uyzz/THH38oLi7O5SpCBTF69GhlZmbq9ddflySFh4crJCREeXl5atu27UWPiIgISVJsbKx27typnJwct59fqVIlHT58+ILlW7/99lv+8+f/9598va5UQe7GXalSJe3evTu/iDrv77EDALyPggGAz1q1apVGjRqlKlWqqFu3bm5fd76YmDx5ssv5d99994rGCQoK0qlTpy44/+eff7o8Dg4OVrVq1ZSVlXVFn/t3sbGx6tKli2bOnKmjR4+qePHi6tKli7744gv9/PPPF7z++PHj+f9/ly5ddOLECb333nsXvO78v6rfcccdysvLu+A148ePl81mU4cOHST986/XlQoKCpKki35t/+6OO+7Q0aNHNW/evPxzubm5evfddxUcHKyWLVsWamwAgCvHkiQAPmHp0qX67bfflJubq2PHjmnVqlVasWKFKlWqpEWLFl3yrs4NGjRQly5dNGHCBP3555/5lwndtWuXpMv/S3eDBg00ZcoUvfbaa6pWrZoiIiLUpk0b1a5dW61atVKDBg1UpkwZbdq0SQsWLFC/fv2uOs/nnntOn3/+uSZMmKDRo0dr9OjRWr16tZo0aaI+ffqodu3aOnnypDZv3qxvv/1WJ0+elCR1795dH3/8sYYMGaIff/xRzZs3V0ZGhr799ls988wz6tSpkzp27KjWrVvrpZde0r59+1S3bl198803+uqrrzRo0CDFxsYWytfrStWrV0/FixfXmDFjlJaWJrvdrjZt2uR3TZw98cQTev/999WzZ08lJyercuXKWrBggf7v//5PEyZMuOQeFgCAh5l5iSYAOH+Z0/NHQECAERUVZdx2223GxIkT8y+n6ezvl1U1DMPIyMgw+vbta5QpU8YIDg427rnnHmPnzp2GJGP06NEXjOd8WdWjR48ad955pxESEmJIyr/c52uvvWY0btzYKF26tBEYGGjUrFnTeP31143s7OxL5nT+sqrz58+/6POtWrUySpUqZZw6dcowDMM4duyY0bdvX6NixYqGv7+/ERUVZdx6663GBx984PK+s2fPGi+99JJRpUqV/Nfdd999xp49e/Jfc/r0aWPw4MFGuXLlDH9/f+P66683xo0bl3/p1YJ+vc5/rY8fP+7y/ot9Hf9
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title(\"Digits Recognition\")\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c5a18b",
   "metadata": {},
   "source": [
    "## 2.鸢尾花识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07a16459",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cb6b5e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93eaff25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b8e7e686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4c34091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f7d8b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e62908b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7cd0d160",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5f19db88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 120, 30)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris.data), len(x_train), len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e3a9bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6240628c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a0e21d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fa87fe2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6ba931a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "        2, 0, 1, 0, 1, 1, 1, 2]),\n",
       " array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "        2, 0, 1, 0, 1, 1, 1, 2]))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60a4514f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "139465f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc96b868",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d2f239a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'True')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwwAAAK9CAYAAACJnusfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA88klEQVR4nO3de5xVdbk/8GdzG0aEQeQmioJyVAwCFTU1uSiJWipZmh0vgGmZSAKSSqWAlKOleRfMG1pa2knJvOQxFJW8ImFpiuK1VG4hcAAdLnv9/ujHtJawlIGBPYPvd6/16jVrr73Ww+61Gx4+z3etQpIkSQAAAKxDg1IXAAAA1F0aBgAAIJeGAQAAyKVhAAAAcmkYAACAXBoGAAAgl4YBAADIpWEAAAByaRgAAIBcGgZgizV48ODo1KlTqcuot2ry+Y0dOzYKhcKmLQiAktAwAPXGpEmTolAoxPTp0zf7tQuFQmZr0aJF9OnTJ+6///7NXkupLF++PMaOHRtTp04tdSkAbEaFJEmSUhcBsD4mTZoUQ4YMieeeey569er1qcevXLkyisVilJWVbfS1C4VCfOlLX4qTTz45kiSJt99+OyZMmBDvv/9+PPjggzFgwICNvkZd8/HPb8GCBdGmTZsYM2ZMjB07NnPsqlWrYtWqVdG0adMSVArAptSo1AUA1LZly5ZFs2bNonHjxrV63l133TVOPPHE6p+/9rWvxR577BFXXnnlFtkw1OTza9SoUTRq5FcKwJbISBJQrw0ePDi23nrreP311+OII46I5s2bxwknnFD92sdn8H/zm9/E3nvvHc2bN48WLVpE9+7d48orr9yga3ft2jVat24dr7/+emZ/VVVVjBkzJrp06RJlZWXRsWPHOOecc6Kqqmqtc/zqV7+KfffdN7baaqvYZpttonfv3vG///u/mWOuu+66+NznPhdlZWXRoUOHGDp0aCxatGitc1177bWx8847R3l5eey7777xxBNPRN++faNv377Vx0ydOjUKhULcdddd8ZOf/CR22GGHaNq0aRxyyCExe/bszPnSn99bb70Vbdq0iYiIcePGVY9mrUka1rWGYdWqVTF+/PjYZZddoqysLDp16hQ/+MEP1vocOnXqFF/5yldi2rRpse+++0bTpk1j5513jttuuy33swdg89EwAPXeqlWrYsCAAdG2bdu49NJL42tf+9o6j3v44Yfjm9/8ZmyzzTZxySWXxMUXXxx9+/aNP//5zxt03cWLF8cHH3wQ22yzTfW+YrEYRx11VFx66aVx5JFHxtVXXx0DBw6Myy+/PL7xjW9k3j9u3Lg46aSTonHjxnHhhRfGuHHjomPHjvHII49UHzN27NgYOnRodOjQIS677LL42te+Ftdff30ceuihsXLlyurjJkyYEGeeeWbssMMO8dOf/jQOOuigGDhwYPzzn/9cZ+0XX3xx3HPPPTFq1KgYPXp0PP3009WN1rq0adMmJkyYEBERX/3qV+OXv/xl/PKXv4xjjjkm9z2nnnpqXHDBBbHXXnvF5ZdfHn369InKyso4/vjj1zp29uzZ8fWvfz2+9KUvxWWXXRbbbLNNDB48OF566aXc8wOwmSQA9cQtt9ySRETy3HPPVe8bNGhQEhHJeeedt9bxgwYNSnbaaafqn88666ykRYsWyapVq2p87YhIvvWtbyXz589P5s2bl0yfPj057LDDkohIfvazn1Uf98tf/jJp0KBB8sQTT2TeP3HixCQikj//+c9JkiTJa6+9ljRo0CD56le/mqxevTpzbLFYTJIkSebNm5c0adIkOfTQQzPHXHPNNUlEJDfffHOSJElSVVWVbLvttsk+++yTrFy5svq4SZMmJRGR9OnTp3rfo48+mkRE0rVr16Sqqqp6/5VXXplERPK3v/2tet/HP7/58+cnEZGMGTNmrc9nzJgxSfpXysyZM5OISE499dTMcaNGjUoiInnkkUeq9+20005JRCSPP/549b558+YlZWVlydlnn73WtQDYvCQMwBbhu9/97qce07Jly1i2bFk8/PDDG3SNm266Kdq0aRNt27aNXr16xZQpU+Kcc86JkSNHVh/z29/+Nrp27Rq77757LFiwoHo7+OCDIyLi0UcfjYiIyZMnR7FYjAsuuCAaNMj+X/Ga0Z4//elPsWLFihg+fHjmmNNOOy1atGhRfYem6dOnx7/+9a847bTTMusITjjhhEz6kTZkyJBo0qRJ9c8HHXRQRES88cYbG/TZfNwDDzwQEZH5bCIizj777IiIte4utccee1TXEPHvRGO33XartXoA2HAaBqDea9SoUeywww6fetwZZ5wRu+66axx++OGxww47xCmnnBJ//OMf1/s6Rx99dDz88MNx//33V8/sL1++PPOX+ddeey1eeumlaNOmTWbbddddIyJi3rx5ERHx+uuvR4MGDWKPPfbIvd7bb78dERG77bZbZn+TJk1i5513rn59zX936dIlc1yjRo1yn6Ow4447Zn5e01h88MEHn/gZrK+33347GjRosFZN7du3j5YtW1bXnFfPmppqqx4ANpxbWgD1XllZ2Vr/Sr8ubdu2jZkzZ8ZDDz0UDz74YDz44INxyy23xMknnxy33nrrp75/hx12iP79+0dExBFHHBGtW7eOM888M/r161c9y18sFqN79+7x85//fJ3n6NixYw3+ZJtOw4YN17k/qeU7ba/vw9w2Vz0A1JyEAfhMadKkSRx55JFx3XXXxeuvvx7f+c534rbbblvrDkHr4zvf+U7ssssu8aMf/aj6L7a77LJLLFy4MA455JDo37//WtuatGCXXXaJYrEYf//733PPv9NOO0VExKxZszL7V6xYEW+++Wb162v+++N/hlWrVsVbb71V4z9Xnpo8yXmnnXaKYrEYr732Wmb/3LlzY9GiRdU1A1D3aRiAz4x//etfmZ8bNGgQn//85yMi1nnL00/TqFGjOPvss+Pll1+O3//+9xERcdxxx8W7774bN9xww1rHf/jhh7Fs2bKIiBg4cGA0aNAgLrzwwigWi5nj1jQf/fv3jyZNmsRVV12V+Zf2m266KRYvXhxf/vKXIyKiV69ese2228YNN9wQq1atqj7u9ttvr9WRnq222ioiYp23dP24I444IiIirrjiisz+NcnLmtoBqPuMJAGfGaeeemosXLgwDj744Nhhhx3i7bffjquvvjp69uwZXbt23aBzDh48OC644IK45JJLYuDAgXHSSSfFXXfdFaeffno8+uijceCBB8bq1avjlVdeibvuuiseeuih6NWrV3Tp0iV++MMfxvjx4+Oggw6KY445JsrKyuK5556LDh06RGVlZbRp0yZGjx4d48aNi8MOOyyOOuqomDVrVlx33XWxzz77VD9ErkmTJjF27NgYNmxYHHzwwXHcccfFW2+9FZMmTYpddtmlRsnAJykvL4899tgj7rzzzth1112jVatW0a1bt+jWrdtax/bo0SMGDRoUv/jFL2LRokXRp0+fePbZZ+PWW2+NgQMHRr9+/WqlJgA2PQ0D8Jlx4oknxi9+8Yu47rrrYtGiRdG+ffv4xje+EWPHjl2vNRDrUl5eHmeeeWaMHTs2pk6dGn379o3JkyfH5ZdfHrfddlvcc889sdVWW8XOO+8cZ511VvXi54iICy+8MDp37hxXX311/PCHP4ytttoqPv/5z8dJJ51UfczYsWOjTZs2cc0118SIESOiVatW8e1vfzsuuuiizJOYzzzzzEiSJC677LIYNWpU9OjRI+6999743ve+F02bNt3wD+1jbrzxxhg2bFiMGDEiVqxYEWPGjFlnw7Dm2J133jkmTZoU99xzT7Rv3z5Gjx4dY8aMqbV6ANj0CokVZQBbpGKxGG3atIljjjlmnSNSALA+rGEA2AJ89NFHa91R6LbbbouFCxdG3759S1MUAFsECQPAFmDq1KkxYsSIOPbYY2PbbbeNGTNmxE033RRdu3aN559/PvOQNgCoCWsYALYAnTp1io4dO8ZVV10VCxcujFatWsXJJ58cF198sWYBgI0iYQAAAHJZwwAAAOTSMAAAALk0DAAAQK4tctHz8iu+U+oSoF5qcc5
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title(\"Iris Recognition\")\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}