notebook/sk4_learning_pattern.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]]\n",
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      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 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]\n",
      "[1 2 1 0 0 0 2 0 1 0 0 0 0 1 1 0 0 2 0 2 0 2 1 0 2 2 2 1 1 2 2 0 1 2 2 0 1\n",
      " 2 1 1 2 2 1 2 2 2 1 0 0 1 1 0 0 1 2 0 1 1 0 0 1 2 0 2 2 0 2 1 2 1 1 2 0 0\n",
      " 0 1 2 1 1 2 1 0 0 0 1 2 2 0 0 2 2 0 1 2 2 2 2 0 1 0 0 2 1 2 1]\n",
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      " 2 2 1 0 2 0 1 2]\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target\n",
    "\n",
    "print(iris_X[:2, :])\n",
    "print(iris_y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_X, iris_y, test_size=0.3)\n",
    "\n",
    "print(y_train)\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print(knn.predict(X_test))\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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