3. Machine Learning & Deep Learning/190924_(3) _ 지도학습 knn.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"190924_(3) ; 지도학습 knn.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"-1nNElb7hRvm","colab_type":"text"},"source":["## 02 knn 알고리즘 시각화"]},{"cell_type":"code","metadata":{"id":"PLkyRgOjdZ_e","colab_type":"code","outputId":"3d83e6ad-8c21-4db9-82d4-163d0fa7f8f2","executionInfo":{"status":"ok","timestamp":1569298250160,"user_tz":-540,"elapsed":5748,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":523}},"source":["#! pip install mglearn"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting mglearn\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/01/8d3630ecc767c9de96a9c46e055f2a3a5f9e14a47d3d0348a36a5005fe67/mglearn-0.1.7.tar.gz (540kB)\n","\r\u001b[K     |▋                               | 10kB 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mglearn) (1.16.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from mglearn) (3.0.3)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from mglearn) (0.21.3)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from mglearn) (0.24.2)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from mglearn) (4.3.0)\n","Requirement already satisfied: cycler in /usr/local/lib/python3.6/dist-packages (from mglearn) (0.10.0)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (from mglearn) (2.4.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (2.5.3)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (1.1.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (2.4.2)\n","Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->mglearn) (1.3.1)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->mglearn) (0.13.2)\n","Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->mglearn) (2018.9)\n","Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->mglearn) (0.46)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler->mglearn) (1.12.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->mglearn) (41.2.0)\n","Building wheels for collected packages: mglearn\n","  Building wheel for mglearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for mglearn: filename=mglearn-0.1.7-py2.py3-none-any.whl size=582705 sha256=1a63bc6651a1bd1811e75ec54cd6ffc66d118cad7bbf62f97ee3dfa26fcf81d0\n","  Stored in directory: /root/.cache/pip/wheels/74/cf/8d/04f4932d15854a36726c6210763c7127e62de28f5c8ddfcf3b\n","Successfully built mglearn\n","Installing collected packages: mglearn\n","Successfully installed mglearn-0.1.7\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MHqCrfJkduK6","colab_type":"code","colab":{}},"source":["import mglearn\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"maeNTBWwhYqR","colab_type":"text"},"source":["### knn의 k가 1인 경우의 알고리즘 (회귀-수치형 값의 예측)"]},{"cell_type":"code","metadata":{"id":"rqY4VzY-e5g6","colab_type":"code","outputId":"8e581daf-eade-4c4f-ef47-72862eebdbb1","executionInfo":{"status":"ok","timestamp":1569299038485,"user_tz":-540,"elapsed":860,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":414}},"source":["mglearn.plots.plot_knn_regression(n_neighbors=1)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAmMAAAGNCAYAAABdbnYSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnjJlStD1RYsWLepz6NChuE8++WT7jh07yvr27VsXaH/j+9QapRQ2b968fc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8juGaMiIiI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xk5OZmIDnZAREgOdlhmcX7LcE1Y0RERASgaqTJ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size 720x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"nf6k_pvOhmDi","colab_type":"text"},"source":["### knn의 k가 3인 경우의 알고리즘 (분류- 범주형 값의 예측)"]},{"cell_type":"code","metadata":{"id":"76hEF4m9g3HK","colab_type":"code","outputId":"b3e0259a-92d9-41f2-a1d8-3a22697deba2","executionInfo":{"status":"ok","timestamp":1569299254676,"user_tz":-540,"elapsed":870,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":327}},"source":["mglearn.plots.plot_knn_classification(n_neighbors=3)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function make_blobs is deprecated; Please import make_blobs directly from scikit-learn\n","  warnings.warn(msg, category=DeprecationWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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rd0xqRKm9hzv0Ka8xEcHKyFsBUXLlzQI0eN0dVremsnJ2ddvaa3HjlqjL5w4YJJjnflyhU9Z84c\nHRQUpAGDjw4dOujFixfr+Ph4k8RgrT799FMN6B49elg6FIsAjuhS5FjpsQthAzIzMzl8+DDh4eGs\nWrWqyPH5QYMGERISQo8ePahUqZKZozSt6dOnM23aNIYOHcoXX3xh6XAsQoZihHAA9+7dY9u2bYSH\nh7Nx40aDberXr09ISAghISEEBATY5HDOyJEjWbx4MZMmTWLmzJmWDsdiJLEL4cCuXr1KREQE4eHh\nREVFGWzTtm1bQkND6d+/P488Uvx8fUt64YUX2LhxI3PnzmXcuHGWDseiJLELIfLRWnPkyJHc2Tk3\nbhieoz9gwABCQ0Pp2bNn7hJ9S2nTpg2HDh0iLCyMIUOGWDQWayCJXQhRKvfv3ycyMpLw8HA2bNhg\nsE3dunVzh3MCAwONPpyTlJTEK8Pe4Iv/fE716tXRWlO/fn2uXbvG1q1befrpp416PFsliV0IUSFx\ncXGsXbuW8PBwDh06ZLBNmzZtCAkJYcCAATz66KPlPtaKFSt4+eWXWbFiBaGhoTg5Za2d3L9/P+3b\nty/3fu2NJHYhhNFprTl69CirV68mPDycuLg4g+369etHSEgIvXv3xt3dvcT9dunZhwPnr9Pu93XY\nvSOrxv2JEydo3ry5UeO3dZLYhRBm8+DBA7Zv3054eDhr16412KZOnTq5q2snTXmXXd9sy33PvVoN\naoXO5drSt9Gpd3Nf79arDzsjvzZ5/LZCErsQwuKuX7/OunXrWLVqFQcPHsz3nnJx45H+03BvGFBo\nuwc/nyAlcg5bN31Jp06dzBSt9TPrjTaEEMKQOnXq8Pbbb3PgwIHcVZGZmZlER0cz4MXniF8/nQdX\nTuTbRpJ6xUliF0KYlVKKwMBA1qxZw1dfriNx65x87yd/M5c1K8MkqVeAi6UDEEI4rps3b4JXPe4c\n2sjD41up3LIvHj5NSExMtHRoNk167EIIixk9/h0exv2Eb8ppNq5ZQcM7p7gdc4wvVq62dGg2TRK7\nEMIiTp48SUpyMiEDX+LIwe/o3r07h77fx6yZM6ha1dPS4dk0mRUjhLCInNWrlshBtkpmxQghrNa0\nadMAilzgJCpGErsQwqxSUlKYPn06gwcPrlAZAlE0SexCCLOqV68eAGFhYRaOxH5JYhdCmM2ePXu4\nc+cOu3btsskbftgKSexCCLPQWtOlSxeqVKlC165dLR2OXZPELoQwi1deeQWQC6bmYJTErpTqpZQ6\np5S6oJSaZIx9CiHsx/Xr1wkLC2PKlClUrVrV0uHYvQrPY1dKOQM/Ad2BWOAwMEhr/WNR28g8diEc\ni8xZNw5zzmNvDVzQWl/UWj8E1gDPGmG/Qgg7sGbNGgCOHz9u4UgchzESez3glzx/j81+TQjh4DIy\nMhg0aBD+/v4EBBSuuy5Mw2wXT5VSw5VSR5RSR+Lj4811WCGEmcTExDBy1Bi8annj5OyMVy1vGvo2\nAiAqKsrC0TkWYyT2q0CDPH+vn/1aPlrrz7TWT2itn/D29jbCYYWjMZQ4Ro4aQ0xMjKVDc3iRkZEE\nBrciIvo6VfrNpMFfN1Kl30ySfZ7ArUpV/vvf/1o6RIdijIunLmRdPO1KVkI/DIRorU8XtY1cPBVl\nFRkZyYBBobj598DNrxsu1R8hPekGqad3kXpqB2tXr6J3796WDtMhxcTEEBjcCs9nJuNWr1mh91Ov\nniFlywyORR2mcePGFojQfpjt4qnWOh14G9gOnAHWFpfUhYCy9b5jYmIYMCgUz2cm49lhCK41fFBO\nzrjW8MGzwxA8n5nMgEGh0nO3kLnzF2Z94BpI6gBu9Zrh5tedeQs+NnNkjssoY+xa621a6z9orRtr\nrf9pjH0K+1XU1/aI6OsEBrciMjIyX3tJHNZtVXg4bn7dim3j5t+dlavCzRSRkJWnwqzK0/uWxGHd\n7iTewqX6I8W2canmTXLiLTNFJCSxC7MqT+9bEod1q+ZVk/SkG8W2Sb8TT1WvmmaKSEhiF2ZVnt63\nJA7rFhoSQurpXcW2ST21k8GhIWaKSEhiF2ZVnt63JA7rNn7saFJP7SD16hmD76dePUPq6Z2MGzPK\nzJE5LknswqzK0/uWxGHdGjduzNrVq0jZMoOUfWGk3Y5DZ6STdjuOlH1hpGyZwdrVq2SqoxlJYhdm\nVZ7etyQO69e7d2+ORR1mYLAPd9dPJnbui9xdP5mBwT4cizosawzMrMILlMpDFig5roosZomJiWHe\ngo9ZuSqc5MRbVPWqyeDQEMaNGSVJXTiE0i5QksQuzC53Falfd9z8u+NSzZv0O/GkntpJ6umdsopU\niCKYs2yvEGUiX9uFMC3psQshhI2QHrsDkaqHQoi8JLHbuLLWXRFC2D8XSwcgyi9v3ZW8M0xca/jg\n2mEIro2eYMCgUCmXKoSDkR67DZOqh0IIQySx2zCpeiiEMEQSuw2TqodCCEMksdswqXoohDBEErsN\nk6qHQghDJLHbMKl6KIQwRKY72rCcqocDBoWSVkzdFZnqKIRjkR67jZO6K0KIgqRWjBBC2AipFSOE\nEA6qQoldKdVfKXVaKZWplCrxU0QIIYTpVbTHfgp4AfjWCLEIIYQwggrNitFanwFQShknGiGEEBVm\ntjF2pdRwpdQRpdSR+Ph4cx1WCCEcTok9dqXULuBRA2/9XWv9VWkPpLX+DPgMsmbFlDpCIYQQZVJi\nYtdaF18+sByioqJuKqV+ruBuagM3jRGPkUlcZWetsUlcZWetsdlLXI+VppFFVp5qrb0rug+l1JHS\nzOc0N4mr7Kw1Nomr7Kw1NkeLq6LTHZ9XSsUCbYGvlVLbjROWEEKI8qrorJiNwEYjxSKEEMIIbHnl\n6WeWDqAIElfZWWtsElfZWWtsDhWXRWrFCCGEMB1b7rELIYQwwOoTu1JqXHY9mlNKqdVKKfcC77sp\npSKUUheUUj8opXytJK5XlFLxSqlj2Y/XzRTXmOyYTiulxhp4XymlFmafrxNKqZZWElcnpVRSnvP1\nrgljWaqUuqGUOpXntZpKqZ1KqfPZf9YoYtuh2W3OK6WGWlFcGXnO3WZjxlVMbKWqFaWU6qWUOpf9\nMzfJiuK6rJQ6mX3OjFputoi45iilzmb/3m1USnkVsW3Fz5fW2mofQD3gElA5++9rgVcKtHkL+Ff2\n84FAhJXE9QqwyMzny5+s+j0eZF0Y3wU0KdCmDxAJKOBJ4AcriasTsNVM56kj0BI4lee12cCk7OeT\ngFkGtqsJXMz+s0b28xqWjiv7vRQLnLNmQFNgL/BEEds5AzHA74BKwHHgcUvHld3uMlDbjOerB+CS\n/XxWET9jRjlfVt9jJysRVFZKuZCVGK4VeP9ZYHn28/VAV2We4jUlxWUJzchK1Pe01unA/8gq0pbX\ns0CYznIQ8FJK+VhBXGajtf4WuFXg5bw/R8uB5wxs2hPYqbW+pbW+DewEellBXCZnKDat9Rmt9bkS\nNm0NXNBaX9RaPwTWkPVvsnRcJlVEXDuyf/4BDgL1DWxqlPNl1Ylda30V+Ai4AsQBSVrrHQWa1QN+\nyW6fDiQBtawgLoAXs792rVdKNTBlTNlOAX9SStVSSnmQ1TsveNzc85UtNvs1S8cF0FYpdVwpFamU\n8jNxTAXV0VrHZT//FahjoI0lzl1p4gJwV1m1mA4qpSyS/ItgiXNWWhrYoZSKUkoNN/OxXyPrm3NB\nRjlfVp3Ys8cTnwUaAXWBKkqpwZaNqtRxbQF8tdYBZPXslmNiOqva5ixgB/ANcAzIMPVxS1LKuI4C\nj2mtWwAfA5vMGmQeOus7sdVNFyshrsd01grGEGC+UkpudFuyDlrrlkBvYKRSqqM5DqqU+juQDqwy\n1TGsOrED3YBLWut4rXUa8CXQrkCbq2T3/rKHRaoDCZaOS2udoLVOzf7rEiDYxDHlHPc/WutgrXVH\n4DbwU4EmuecrW/3s1ywal9b6jtY6Jfv5NsBVKVXb1HHlcT1nSCr7zxsG2lji3JUmrpxvkWitL5I1\nthxk4rhKyyI/b6WR55zdIGuhZWtTH1Mp9QrwZyA0+4O6IKOcL2tP7FeAJ5VSHtnj5l2BMwXabAZy\nZif0A3YXccLMGleBceu+Bd83FaXUI9l/NiRrHDu8QJPNwMvZs2OeJGsYKQ4TKykupdSjOddGlFKt\nyfrZNPUHdF55f46GAoYql24HeiilamR/a+uR/ZpF48qOxy37eW2gPfCjieMqrcPA75VSjZRSlcia\n4GD0WTtlpZSqopSqmvOcrP/LU8VvVeFj9gImAH211veKaGac82WKK8LGfADvAWfJOukrADdgevbJ\nAXAH1gEXgEPA76wkrpnAabKuau8B/mimuPaR9Ut9HOia/dqbwJvZzxXwCVlX3k9SzKwBM8f1dp7z\ndRBoZ8JYVpN1bSSNrDHMYWRdl/kvcJ6sWTs1s9s+ASzJs+1r2T9rF4BXrSEusr4tnsw+dyeBYWY6\nZ89nP08FrgPbs9vWBbbl2bYPWd/QYsgq923xuMiadXI8+3HaTHFdIGv8/Fj2418F4zLW+ZKVp0II\nYWesfShGCCFEGUliF0IIOyOJXQgh7IwkdiGEsDOS2IUQws5IYhdCCDsjiV0IIeyMJHYhhLAz/x+k\nBxVXwgUs2AAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"T2SchdmEhkm7","colab_type":"code","outputId":"6c6703e0-9dfb-4e9c-97de-88b28eb4509f","executionInfo":{"status":"ok","timestamp":1569299278670,"user_tz":-540,"elapsed":897,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":327}},"source":["mglearn.plots.plot_knn_classification(n_neighbors=5)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function make_blobs is deprecated; Please import make_blobs directly from scikit-learn\n","  warnings.warn(msg, category=DeprecationWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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Rt23b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\nx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4Jvkx74PlCXzl1j7rhsnxMXR4mOY/Pdb2Dkk6Ts+hGdlUmpUqXo3bs3PXv2pHbt2tLr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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"-vV-OSe5h6nl","colab_type":"text"},"source":["## 03 유방암 데이터 셋 실습"]},{"cell_type":"code","metadata":{"id":"urJwA4Xohqdb","colab_type":"code","colab":{}},"source":["from sklearn.datasets import load_breast_cancer\n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5n7WXJ-Mh0mI","colab_type":"code","outputId":"6d1d3fcc-a983-4686-b9cd-06d3b71a65a6","executionInfo":{"status":"ok","timestamp":1569299436380,"user_tz":-540,"elapsed":487,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":933}},"source":["cancer = load_breast_cancer()\n","\n","print(cancer.keys())\n","print(cancer.items())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n","dict_items([('data', array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n","        1.189e-01],\n","       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n","        8.902e-02],\n","       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n","        8.758e-02],\n","       ...,\n","       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n","        7.820e-02],\n","       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n","        1.240e-01],\n","       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n","        7.039e-02]])), ('target', array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n","       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n","       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n","       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n","       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n","       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n","       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n","       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n","       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n","       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n","       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n","       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n","       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n","       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n","       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n","       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n","       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n","       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 0, 0, 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, 0, 0, 0, 0, 0, 0, 1])), ('target_names', array(['malignant', 'benign'], dtype='<U9')), ('DESCR', '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry \\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.'), ('feature_names', array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n","       'mean smoothness', 'mean compactness', 'mean concavity',\n","       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n","       'radius error', 'texture error', 'perimeter error', 'area error',\n","       'smoothness error', 'compactness error', 'concavity error',\n","       'concave points error', 'symmetry error',\n","       'fractal dimension error', 'worst radius', 'worst texture',\n","       'worst perimeter', 'worst area', 'worst smoothness',\n","       'worst compactness', 'worst concavity', 'worst concave points',\n","       'worst symmetry', 'worst fractal dimension'], dtype='<U23')), ('filename', '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/breast_cancer.csv')])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UPZUTd7hjHn9","colab_type":"text"},"source":["### 데이터 셋 나누기"]},{"cell_type":"code","metadata":{"id":"sUxF37YqiRD7","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import train_test_split\n","\n","X = cancer.data\n","Y = cancer.target\n","X_train, X_test, y_train, y_test = train_test_split(X, Y,\n","                                                    stratify = cancer.target,\n","                                                    random_state=77)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZZXh3IFjjNGU","colab_type":"text"},"source":["### 머신러닝 모델 만들기 / 예측하기\n","(1) 모델 만들기\n","\n","(2) 모델 학습시키기 model.fit()\n","\n","(3) 모델 예측 model.predict()"]},{"cell_type":"code","metadata":{"id":"xz8CXYAKiZ37","colab_type":"code","outputId":"73758456-cadc-4dcc-c97f-584980b258d5","executionInfo":{"status":"ok","timestamp":1569299916652,"user_tz":-540,"elapsed":574,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":148}},"source":["from sklearn.neighbors import KNeighborsClassifier\n","\n","#(1)\n","model = KNeighborsClassifier(n_neighbors=3)\n","#(2)\n","model.fit(X_train, y_train)\n","#(3)\n","pred = model.predict(X_test)\n","pred"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n","       0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0,\n","       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n","       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n","       1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n","       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1])"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"L9AkemNkkkBy","colab_type":"text"},"source":["### 정확도 확인"]},{"cell_type":"code","metadata":{"id":"sMLNcQZ-kEq-","colab_type":"code","outputId":"d4777560-163a-498f-fd68-a1d2423a91db","executionInfo":{"status":"ok","timestamp":1569300007792,"user_tz":-540,"elapsed":495,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":36}},"source":["(pred == y_test).sum()/(len(pred))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9090909090909091"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"AptTXafDkckL","colab_type":"code","outputId":"a2f8820b-7a82-424a-e5ca-3a2aeae0faf9","executionInfo":{"status":"ok","timestamp":1569300229951,"user_tz":-540,"elapsed":550,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":55}},"source":["acc_tr = model.score(X_train, y_train)\n","acc_test = model.score(X_test, y_test)\n","\n","print(\"훈련 데이터 셋의 정확도 : {:2f}\" .format(acc_tr))\n","print(\"테스트 데이터 셋의 정확도 : {:2f}\" .format(acc_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["훈련 데이터 셋의 정확도 : 0.955399\n","테스트 데이터 셋의 정확도 : 0.909091\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xW8ljH78lOsv","colab_type":"code","outputId":"ad6a12f9-0f15-4417-9c1a-d4a28a6c030f","executionInfo":{"status":"ok","timestamp":1569300507528,"user_tz":-540,"elapsed":536,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["training_accuracy = []\n","test_accuracy = []\n","k_num = range(1,11)\n","\n","for n in k_num:\n","  model = KNeighborsClassifier(n_neighbors=n)\n","  model.fit(X_train, y_train)\n","  \n","  acc_tr = model.score(X_train, y_train)\n","  acc_test = model.score(X_test, y_test)\n","\n","  training_accuracy.append(acc_tr)\n","  test_accuracy.append(acc_test)\n","\n","  print(\"k : {}, tr_acc : {:.2f}, test_acc : {:.2f}\" \n","        .format(n, acc_tr, acc_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["k : 1, tr_acc : 1.00, test_acc : 0.89\n","k : 2, tr_acc : 0.97, test_acc : 0.90\n","k : 3, tr_acc : 0.96, test_acc : 0.91\n","k : 4, tr_acc : 0.95, test_acc : 0.90\n","k : 5, tr_acc : 0.95, test_acc : 0.92\n","k : 6, tr_acc : 0.96, test_acc : 0.90\n","k : 7, tr_acc : 0.95, test_acc : 0.91\n","k : 8, tr_acc : 0.95, test_acc : 0.91\n","k : 9, tr_acc : 0.95, test_acc : 0.91\n","k : 10, tr_acc : 0.94, test_acc : 0.91\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"awkOGiv3mQRg","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}