![\"\"](\"data:image\/svg+xml;base64,PCEtLUFyZ29uTG9hZGluZy0tPgo8c3ZnIHdpZHRoPSIxIiBoZWlnaHQ9IjEiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgc3Ryb2tlPSIjZmZmZmZmMDAiPjxnPjwvZz4KPC9zdmc+\")
<\/div><\/p>\n
1.4 \u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u6d41\u7a0b<\/h1>\n
\u6b64\u5904\u53ea\u662f\u7b80\u5355\u7684\u5e26\u540c\u5b66\u4eec\u4e86\u89e3\u4e0b\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u7684\u6d41\u7a0b\uff0c\u5373\u4ee5\u4e0b6\u4e2a\u6b65\u9aa4\uff1a<\/p>\n
1. \u6536\u96c6\u6570\u636e\n2. \u6570\u636e\u9884\u5904\u7406\n3. \u8bad\u7ec3\u6a21\u578b\n4. \u6d4b\u8bd5\u6a21\u578b\n5. \u4f18\u5316\u6a21\u578b\n6. \u6301\u4e45\u5316\u6a21\u578b<\/code><\/pre>\n\u4e4b\u540e\u4f1a\u8be6\u7ec6\u8bb2\u89e3\u8be5\u6d41\u7a0b\u7684\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u3002<\/p>\n
1.4.1 \u6536\u96c6\u6570\u636e<\/h2>\n
\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\uff0c\u65e0\u8bba\u662f\u76d1\u7763\u5b66\u4e60\u8fd8\u662f\u65e0\u76d1\u7763\u5b66\u4e60\uff0c\u7b2c\u4e00\u6b65\u90fd\u662f\u83b7\u53d6\u6570\u636e\uff0c\u6b64\u5904\u4e3a\u4e86\u5e26\u5927\u5bb6\u5bf9\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u6709\u4e00\u4e2a\u7b80\u5355\u7684\u4e86\u89e3\uff0c\u6240\u4ee5\u5229\u7528sklearn\u81ea\u5e26\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u4f5c\u5c55\u793a\uff0c\u4e4b\u540e\u518d\u6536\u96c6\u6570\u636e\u5c0f\u8282\u4f1a\u8be6\u7ec6\u4ecb\u7ecd\u6536\u96c6\u6570\u636e\u7684\u51e0\u79cd\u65b9\u5f0f\u3002<\/p>\n
import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\nfrom sklearn import datasets\n%matplotlib inline\nfont = FontProperties(fname='\/Library\/Fonts\/Heiti.ttc')\n\niris = datasets.load_iris()\niris<\/code><\/pre>\n{'data': array([[5.1, 3.5, 1.4, 0.2],\n [4.9, 3. , 1.4, 0.2],\n [4.7, 3.2, 1.3, 0.2],\n [4.6, 3.1, 1.5, 0.2],\n [5. , 3.6, 1.4, 0.2],\n [5.4, 3.9, 1.7, 0.4],\n [4.6, 3.4, 1.4, 0.3],\n [5. , 3.4, 1.5, 0.2],\n [4.4, 2.9, 1.4, 0.2],\n [4.9, 3.1, 1.5, 0.1],\n [5.4, 3.7, 1.5, 0.2],\n [4.8, 3.4, 1.6, 0.2],\n [4.8, 3. , 1.4, 0.1],\n [4.3, 3. , 1.1, 0.1],\n [5.8, 4. , 1.2, 0.2],\n [5.7, 4.4, 1.5, 0.4],\n [5.4, 3.9, 1.3, 0.4],\n [5.1, 3.5, 1.4, 0.3],\n [5.7, 3.8, 1.7, 0.3],\n [5.1, 3.8, 1.5, 0.3],\n [5.4, 3.4, 1.7, 0.2],\n [5.1, 3.7, 1.5, 0.4],\n [4.6, 3.6, 1. , 0.2],\n [5.1, 3.3, 1.7, 0.5],\n [4.8, 3.4, 1.9, 0.2],\n [5. , 3. , 1.6, 0.2],\n [5. , 3.4, 1.6, 0.4],\n [5.2, 3.5, 1.5, 0.2],\n [5.2, 3.4, 1.4, 0.2],\n [4.7, 3.2, 1.6, 0.2],\n [4.8, 3.1, 1.6, 0.2],\n [5.4, 3.4, 1.5, 0.4],\n [5.2, 4.1, 1.5, 0.1],\n [5.5, 4.2, 1.4, 0.2],\n [4.9, 3.1, 1.5, 0.2],\n [5. , 3.2, 1.2, 0.2],\n [5.5, 3.5, 1.3, 0.2],\n [4.9, 3.6, 1.4, 0.1],\n [4.4, 3. , 1.3, 0.2],\n [5.1, 3.4, 1.5, 0.2],\n [5. , 3.5, 1.3, 0.3],\n [4.5, 2.3, 1.3, 0.3],\n [4.4, 3.2, 1.3, 0.2],\n [5. , 3.5, 1.6, 0.6],\n [5.1, 3.8, 1.9, 0.4],\n [4.8, 3. , 1.4, 0.3],\n [5.1, 3.8, 1.6, 0.2],\n [4.6, 3.2, 1.4, 0.2],\n [5.3, 3.7, 1.5, 0.2],\n [5. , 3.3, 1.4, 0.2],\n [7. , 3.2, 4.7, 1.4],\n [6.4, 3.2, 4.5, 1.5],\n [6.9, 3.1, 4.9, 1.5],\n [5.5, 2.3, 4. , 1.3],\n [6.5, 2.8, 4.6, 1.5],\n [5.7, 2.8, 4.5, 1.3],\n [6.3, 3.3, 4.7, 1.6],\n [4.9, 2.4, 3.3, 1. ],\n [6.6, 2.9, 4.6, 1.3],\n [5.2, 2.7, 3.9, 1.4],\n [5. , 2. , 3.5, 1. ],\n [5.9, 3. , 4.2, 1.5],\n [6. , 2.2, 4. , 1. ],\n [6.1, 2.9, 4.7, 1.4],\n [5.6, 2.9, 3.6, 1.3],\n [6.7, 3.1, 4.4, 1.4],\n [5.6, 3. , 4.5, 1.5],\n [5.8, 2.7, 4.1, 1. ],\n [6.2, 2.2, 4.5, 1.5],\n [5.6, 2.5, 3.9, 1.1],\n [5.9, 3.2, 4.8, 1.8],\n [6.1, 2.8, 4. , 1.3],\n [6.3, 2.5, 4.9, 1.5],\n [6.1, 2.8, 4.7, 1.2],\n [6.4, 2.9, 4.3, 1.3],\n [6.6, 3. , 4.4, 1.4],\n [6.8, 2.8, 4.8, 1.4],\n [6.7, 3. , 5. , 1.7],\n [6. , 2.9, 4.5, 1.5],\n [5.7, 2.6, 3.5, 1. ],\n [5.5, 2.4, 3.8, 1.1],\n [5.5, 2.4, 3.7, 1. ],\n [5.8, 2.7, 3.9, 1.2],\n [6. , 2.7, 5.1, 1.6],\n [5.4, 3. , 4.5, 1.5],\n [6. , 3.4, 4.5, 1.6],\n [6.7, 3.1, 4.7, 1.5],\n [6.3, 2.3, 4.4, 1.3],\n [5.6, 3. , 4.1, 1.3],\n [5.5, 2.5, 4. , 1.3],\n [5.5, 2.6, 4.4, 1.2],\n [6.1, 3. , 4.6, 1.4],\n [5.8, 2.6, 4. , 1.2],\n [5. , 2.3, 3.3, 1. ],\n [5.6, 2.7, 4.2, 1.3],\n [5.7, 3. , 4.2, 1.2],\n [5.7, 2.9, 4.2, 1.3],\n [6.2, 2.9, 4.3, 1.3],\n [5.1, 2.5, 3. , 1.1],\n [5.7, 2.8, 4.1, 1.3],\n [6.3, 3.3, 6. , 2.5],\n [5.8, 2.7, 5.1, 1.9],\n [7.1, 3. , 5.9, 2.1],\n [6.3, 2.9, 5.6, 1.8],\n [6.5, 3. , 5.8, 2.2],\n [7.6, 3. , 6.6, 2.1],\n [4.9, 2.5, 4.5, 1.7],\n [7.3, 2.9, 6.3, 1.8],\n [6.7, 2.5, 5.8, 1.8],\n [7.2, 3.6, 6.1, 2.5],\n [6.5, 3.2, 5.1, 2. ],\n [6.4, 2.7, 5.3, 1.9],\n [6.8, 3. , 5.5, 2.1],\n [5.7, 2.5, 5. , 2. ],\n [5.8, 2.8, 5.1, 2.4],\n [6.4, 3.2, 5.3, 2.3],\n [6.5, 3. , 5.5, 1.8],\n [7.7, 3.8, 6.7, 2.2],\n [7.7, 2.6, 6.9, 2.3],\n [6. , 2.2, 5. , 1.5],\n [6.9, 3.2, 5.7, 2.3],\n [5.6, 2.8, 4.9, 2. ],\n [7.7, 2.8, 6.7, 2. ],\n [6.3, 2.7, 4.9, 1.8],\n [6.7, 3.3, 5.7, 2.1],\n [7.2, 3.2, 6. , 1.8],\n [6.2, 2.8, 4.8, 1.8],\n [6.1, 3. , 4.9, 1.8],\n [6.4, 2.8, 5.6, 2.1],\n [7.2, 3. , 5.8, 1.6],\n [7.4, 2.8, 6.1, 1.9],\n [7.9, 3.8, 6.4, 2. ],\n [6.4, 2.8, 5.6, 2.2],\n [6.3, 2.8, 5.1, 1.5],\n [6.1, 2.6, 5.6, 1.4],\n [7.7, 3. , 6.1, 2.3],\n [6.3, 3.4, 5.6, 2.4],\n [6.4, 3.1, 5.5, 1.8],\n [6. , 3. , 4.8, 1.8],\n [6.9, 3.1, 5.4, 2.1],\n [6.7, 3.1, 5.6, 2.4],\n [6.9, 3.1, 5.1, 2.3],\n [5.8, 2.7, 5.1, 1.9],\n [6.8, 3.2, 5.9, 2.3],\n [6.7, 3.3, 5.7, 2.5],\n [6.7, 3. , 5.2, 2.3],\n [6.3, 2.5, 5. , 1.9],\n [6.5, 3. , 5.2, 2. ],\n [6.2, 3.4, 5.4, 2.3],\n [5.9, 3. , 5.1, 1.8]]),\n 'target': 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]),\n 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<\/pre>\nX = iris.data\n# \u603b\u5171\u6709150\u4e2a\u6837\u672c\u6570\u636e\uff0c\u6b64\u5904\u53ea\u6253\u53705\u4e2a\n'X\u7684\u4e2a\u6570:{}'.format(len(X)), 'X:{}'.format(X[0:5])<\/code><\/pre>\n('X\u7684\u4e2a\u6570:150',\n 'X:[[5.1 3.5 1.4 0.2]\\n [4.9 3. 1.4 0.2]\\n [4.7 3.2 1.3 0.2]\\n [4.6 3.1 1.5 0.2]\\n [5. 3.6 1.4 0.2]]')<\/code><\/pre>\ny = iris.target\n'y\u7684\u4e2a\u6570:{}'.format(len(y)), 'y:{}'.format(y)<\/code><\/pre>\n('y\u7684\u4e2a\u6570:150',\n 'y:[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\\n 2 2]')<\/code><\/pre>\n# pandas\u53ef\u89c6\u5316\u6570\u636e\ndf = pd.DataFrame(X, columns=iris.feature_names)\ndf['target'] = y\ndf.plot(figsize=(10, 8))\nplt.show()<\/code><\/pre>\n<\/div><\/p>\n
# matplotlib\u53ef\u89c6\u5316\n# matplotlib\u9002\u5408\u4e8c\u7ef4\u53ef\u89c6\u5316\uff0c\u56e0\u6b64\u53ea\u9009\u7279\u5f811\u30012\uff0c\u5373\u843c\u7247\u957f\u5ea6\u3001\u843c\u7247\u5bbd\u5ea6\n# \u53d6\u6240\u6709\u884c\u7684\u7b2c1\uff0c2\u5217\u7279\u5f81\nX_ = X[:, [0, 1]]\n\n# \u53d6\u51fa\u5c71\u9e22\u5c3e\u6570\u636e\nplt.scatter(X_[0:50, 0], X_[0:50, 1], color='r', label='\u5c71\u9e22\u5c3e', s=10)\n# \u53d6\u51fa\u6742\u8272\u9e22\u5c3e\u6570\u636e\nplt.scatter(X_[50:100, 0], X_[50:100, 1], color='g', label='\u6742\u8272\u9e22\u5c3e', s=50)\n# \u53d6\u51fa\u7ef4\u5409\u5c3c\u4e9a\u9e22\u5c3e\nplt.scatter(X_[100:150, 0], X_[100:150, 1], color='b', label='\u7ef4\u5409\u5c3c\u4e9a\u9e22\u5c3e', s=100)\n\nplt.legend(prop=font)\nplt.xlabel('\u843c\u7247\u957f\u5ea6', fontproperties=font, fontsize=15)\nplt.ylabel('\u843c\u7247\u5bbd\u5ea6', fontproperties=font, fontsize=15)\nplt.title('\u843c\u7247\u957f\u5ea6-\u843c\u7247\u5bbd\u5ea6', fontproperties=font, fontsize=20)\nplt.show()<\/code><\/pre>\n<\/div><\/p>\n
1.4.2 \u6570\u636e\u9884\u5904\u7406<\/h2>\n
\u53ef\u4ee5\u53d1\u73b0\u9e22\u5c3e\u82b1\u6570\u636e\u7684\u67d0\u4e00\u4e2a\u7279\u5f81\u7684\u7279\u5f81\u503c\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u5dee\u8ddd\u975e\u5e38\u5927\uff0c\u4e3a\u4e86\u89e3\u51b3\u4e0a\u8ff0\u76f8\u540c\u6743\u91cd\u7279\u5f81\u4e0d\u540c\u5c3a\u5ea6\u7684\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u6700\u5c0f-\u6700\u5927\u6807\u51c6\u5316\u505a\u5904\u7406\uff0c\u628a\u4ed6\u4eec\u4e24\u4e2a\u503c\u538b\u7f29\u5728$[0-1]$\u533a\u95f4\u5185\u3002<\/p>\n
\u6700\u5c0f-\u6700\u5927\u6807\u51c6\u5316\u516c\u5f0f\uff1a
\n$$
\nx{norm}^{(i)}={\\frac{x^{(i)}-x<\/em>{min}}{x{max}-x<\/em>{min}}}
\n$$
\n\u5176\u4e2d$i=1,2,\\cdots,m$\uff1b$m$\u4e3a\u6837\u672c\u4e2a\u6570\uff1b$x{min},x<\/em>{max}$\u5206\u522b\u662f\u67d0\u4e2a\u7684\u7279\u5f81\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\nfrom sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\n# scaler.fit_transform(X) # \u7b49\u540c\u4e8e\u5148fit()\u540etransform()\nscaler = scaler.fit(X)\nprint(X)\nX1 = scaler.transform(X)\nX1<\/code><\/pre>\n[[5.1 3.5 1.4 0.2]\n [4.9 3. 1.4 0.2]\n [4.7 3.2 1.3 0.2]\n [4.6 3.1 1.5 0.2]\n [5. 3.6 1.4 0.2]\n [5.4 3.9 1.7 0.4]\n [4.6 3.4 1.4 0.3]\n [5. 3.4 1.5 0.2]\n [4.4 2.9 1.4 0.2]\n [4.9 3.1 1.5 0.1]\n [5.4 3.7 1.5 0.2]\n [4.8 3.4 1.6 0.2]\n [4.8 3. 1.4 0.1]\n [4.3 3. 1.1 0.1]\n [5.8 4. 1.2 0.2]\n [5.7 4.4 1.5 0.4]\n [5.4 3.9 1.3 0.4]\n [5.1 3.5 1.4 0.3]\n [5.7 3.8 1.7 0.3]\n [5.1 3.8 1.5 0.3]\n [5.4 3.4 1.7 0.2]\n [5.1 3.7 1.5 0.4]\n [4.6 3.6 1. 0.2]\n [5.1 3.3 1.7 0.5]\n [4.8 3.4 1.9 0.2]\n [5. 3. 1.6 0.2]\n [5. 3.4 1.6 0.4]\n [5.2 3.5 1.5 0.2]\n [5.2 3.4 1.4 0.2]\n [4.7 3.2 1.6 0.2]\n [4.8 3.1 1.6 0.2]\n [5.4 3.4 1.5 0.4]\n [5.2 4.1 1.5 0.1]\n [5.5 4.2 1.4 0.2]\n [4.9 3.1 1.5 0.2]\n [5. 3.2 1.2 0.2]\n [5.5 3.5 1.3 0.2]\n [4.9 3.6 1.4 0.1]\n [4.4 3. 1.3 0.2]\n [5.1 3.4 1.5 0.2]\n [5. 3.5 1.3 0.3]\n [4.5 2.3 1.3 0.3]\n [4.4 3.2 1.3 0.2]\n [5. 3.5 1.6 0.6]\n [5.1 3.8 1.9 0.4]\n [4.8 3. 1.4 0.3]\n [5.1 3.8 1.6 0.2]\n [4.6 3.2 1.4 0.2]\n [5.3 3.7 1.5 0.2]\n [5. 3.3 1.4 0.2]\n [7. 3.2 4.7 1.4]\n [6.4 3.2 4.5 1.5]\n [6.9 3.1 4.9 1.5]\n [5.5 2.3 4. 1.3]\n [6.5 2.8 4.6 1.5]\n [5.7 2.8 4.5 1.3]\n [6.3 3.3 4.7 1.6]\n [4.9 2.4 3.3 1. ]\n [6.6 2.9 4.6 1.3]\n [5.2 2.7 3.9 1.4]\n [5. 2. 3.5 1. ]\n [5.9 3. 4.2 1.5]\n [6. 2.2 4. 1. ]\n [6.1 2.9 4.7 1.4]\n [5.6 2.9 3.6 1.3]\n [6.7 3.1 4.4 1.4]\n [5.6 3. 4.5 1.5]\n [5.8 2.7 4.1 1. ]\n [6.2 2.2 4.5 1.5]\n [5.6 2.5 3.9 1.1]\n [5.9 3.2 4.8 1.8]\n [6.1 2.8 4. 1.3]\n [6.3 2.5 4.9 1.5]\n [6.1 2.8 4.7 1.2]\n [6.4 2.9 4.3 1.3]\n [6.6 3. 4.4 1.4]\n [6.8 2.8 4.8 1.4]\n [6.7 3. 5. 1.7]\n [6. 2.9 4.5 1.5]\n [5.7 2.6 3.5 1. ]\n [5.5 2.4 3.8 1.1]\n [5.5 2.4 3.7 1. ]\n [5.8 2.7 3.9 1.2]\n [6. 2.7 5.1 1.6]\n [5.4 3. 4.5 1.5]\n [6. 3.4 4.5 1.6]\n [6.7 3.1 4.7 1.5]\n [6.3 2.3 4.4 1.3]\n [5.6 3. 4.1 1.3]\n [5.5 2.5 4. 1.3]\n [5.5 2.6 4.4 1.2]\n [6.1 3. 4.6 1.4]\n [5.8 2.6 4. 1.2]\n [5. 2.3 3.3 1. ]\n [5.6 2.7 4.2 1.3]\n [5.7 3. 4.2 1.2]\n [5.7 2.9 4.2 1.3]\n [6.2 2.9 4.3 1.3]\n [5.1 2.5 3. 1.1]\n [5.7 2.8 4.1 1.3]\n [6.3 3.3 6. 2.5]\n [5.8 2.7 5.1 1.9]\n [7.1 3. 5.9 2.1]\n [6.3 2.9 5.6 1.8]\n [6.5 3. 5.8 2.2]\n [7.6 3. 6.6 2.1]\n [4.9 2.5 4.5 1.7]\n [7.3 2.9 6.3 1.8]\n [6.7 2.5 5.8 1.8]\n [7.2 3.6 6.1 2.5]\n [6.5 3.2 5.1 2. ]\n [6.4 2.7 5.3 1.9]\n [6.8 3. 5.5 2.1]\n [5.7 2.5 5. 2. ]\n [5.8 2.8 5.1 2.4]\n [6.4 3.2 5.3 2.3]\n [6.5 3. 5.5 1.8]\n [7.7 3.8 6.7 2.2]\n [7.7 2.6 6.9 2.3]\n [6. 2.2 5. 1.5]\n [6.9 3.2 5.7 2.3]\n [5.6 2.8 4.9 2. ]\n [7.7 2.8 6.7 2. ]\n [6.3 2.7 4.9 1.8]\n [6.7 3.3 5.7 2.1]\n [7.2 3.2 6. 1.8]\n [6.2 2.8 4.8 1.8]\n [6.1 3. 4.9 1.8]\n [6.4 2.8 5.6 2.1]\n [7.2 3. 5.8 1.6]\n [7.4 2.8 6.1 1.9]\n [7.9 3.8 6.4 2. ]\n [6.4 2.8 5.6 2.2]\n [6.3 2.8 5.1 1.5]\n [6.1 2.6 5.6 1.4]\n [7.7 3. 6.1 2.3]\n [6.3 3.4 5.6 2.4]\n [6.4 3.1 5.5 1.8]\n [6. 3. 4.8 1.8]\n [6.9 3.1 5.4 2.1]\n [6.7 3.1 5.6 2.4]\n [6.9 3.1 5.1 2.3]\n [5.8 2.7 5.1 1.9]\n [6.8 3.2 5.9 2.3]\n [6.7 3.3 5.7 2.5]\n [6.7 3. 5.2 2.3]\n [6.3 2.5 5. 1.9]\n [6.5 3. 5.2 2. ]\n [6.2 3.4 5.4 2.3]\n [5.9 3. 5.1 1.8]]\n\narray([[0.22222222, 0.625 , 0.06779661, 0.04166667],\n [0.16666667, 0.41666667, 0.06779661, 0.04166667],\n [0.11111111, 0.5 , 0.05084746, 0.04166667],\n [0.08333333, 0.45833333, 0.08474576, 0.04166667],\n [0.19444444, 0.66666667, 0.06779661, 0.04166667],\n [0.30555556, 0.79166667, 0.11864407, 0.125 ],\n [0.08333333, 0.58333333, 0.06779661, 0.08333333],\n [0.19444444, 0.58333333, 0.08474576, 0.04166667],\n [0.02777778, 0.375 , 0.06779661, 0.04166667],\n [0.16666667, 0.45833333, 0.08474576, 0. ],\n [0.30555556, 0.70833333, 0.08474576, 0.04166667],\n [0.13888889, 0.58333333, 0.10169492, 0.04166667],\n [0.13888889, 0.41666667, 0.06779661, 0. ],\n [0. , 0.41666667, 0.01694915, 0. ],\n [0.41666667, 0.83333333, 0.03389831, 0.04166667],\n [0.38888889, 1. , 0.08474576, 0.125 ],\n [0.30555556, 0.79166667, 0.05084746, 0.125 ],\n [0.22222222, 0.625 , 0.06779661, 0.08333333],\n [0.38888889, 0.75 , 0.11864407, 0.08333333],\n [0.22222222, 0.75 , 0.08474576, 0.08333333],\n [0.30555556, 0.58333333, 0.11864407, 0.04166667],\n [0.22222222, 0.70833333, 0.08474576, 0.125 ],\n [0.08333333, 0.66666667, 0. , 0.04166667],\n [0.22222222, 0.54166667, 0.11864407, 0.16666667],\n [0.13888889, 0.58333333, 0.15254237, 0.04166667],\n [0.19444444, 0.41666667, 0.10169492, 0.04166667],\n [0.19444444, 0.58333333, 0.10169492, 0.125 ],\n [0.25 , 0.625 , 0.08474576, 0.04166667],\n [0.25 , 0.58333333, 0.06779661, 0.04166667],\n [0.11111111, 0.5 , 0.10169492, 0.04166667],\n [0.13888889, 0.45833333, 0.10169492, 0.04166667],\n [0.30555556, 0.58333333, 0.08474576, 0.125 ],\n [0.25 , 0.875 , 0.08474576, 0. ],\n [0.33333333, 0.91666667, 0.06779661, 0.04166667],\n [0.16666667, 0.45833333, 0.08474576, 0.04166667],\n [0.19444444, 0.5 , 0.03389831, 0.04166667],\n [0.33333333, 0.625 , 0.05084746, 0.04166667],\n [0.16666667, 0.66666667, 0.06779661, 0. ],\n [0.02777778, 0.41666667, 0.05084746, 0.04166667],\n [0.22222222, 0.58333333, 0.08474576, 0.04166667],\n [0.19444444, 0.625 , 0.05084746, 0.08333333],\n [0.05555556, 0.125 , 0.05084746, 0.08333333],\n [0.02777778, 0.5 , 0.05084746, 0.04166667],\n [0.19444444, 0.625 , 0.10169492, 0.20833333],\n [0.22222222, 0.75 , 0.15254237, 0.125 ],\n [0.13888889, 0.41666667, 0.06779661, 0.08333333],\n [0.22222222, 0.75 , 0.10169492, 0.04166667],\n [0.08333333, 0.5 , 0.06779661, 0.04166667],\n [0.27777778, 0.70833333, 0.08474576, 0.04166667],\n [0.19444444, 0.54166667, 0.06779661, 0.04166667],\n [0.75 , 0.5 , 0.62711864, 0.54166667],\n [0.58333333, 0.5 , 0.59322034, 0.58333333],\n [0.72222222, 0.45833333, 0.66101695, 0.58333333],\n [0.33333333, 0.125 , 0.50847458, 0.5 ],\n [0.61111111, 0.33333333, 0.61016949, 0.58333333],\n [0.38888889, 0.33333333, 0.59322034, 0.5 ],\n [0.55555556, 0.54166667, 0.62711864, 0.625 ],\n [0.16666667, 0.16666667, 0.38983051, 0.375 ],\n [0.63888889, 0.375 , 0.61016949, 0.5 ],\n [0.25 , 0.29166667, 0.49152542, 0.54166667],\n [0.19444444, 0. , 0.42372881, 0.375 ],\n [0.44444444, 0.41666667, 0.54237288, 0.58333333],\n [0.47222222, 0.08333333, 0.50847458, 0.375 ],\n [0.5 , 0.375 , 0.62711864, 0.54166667],\n [0.36111111, 0.375 , 0.44067797, 0.5 ],\n [0.66666667, 0.45833333, 0.57627119, 0.54166667],\n [0.36111111, 0.41666667, 0.59322034, 0.58333333],\n [0.41666667, 0.29166667, 0.52542373, 0.375 ],\n [0.52777778, 0.08333333, 0.59322034, 0.58333333],\n [0.36111111, 0.20833333, 0.49152542, 0.41666667],\n [0.44444444, 0.5 , 0.6440678 , 0.70833333],\n [0.5 , 0.33333333, 0.50847458, 0.5 ],\n [0.55555556, 0.20833333, 0.66101695, 0.58333333],\n [0.5 , 0.33333333, 0.62711864, 0.45833333],\n [0.58333333, 0.375 , 0.55932203, 0.5 ],\n [0.63888889, 0.41666667, 0.57627119, 0.54166667],\n [0.69444444, 0.33333333, 0.6440678 , 0.54166667],\n [0.66666667, 0.41666667, 0.6779661 , 0.66666667],\n [0.47222222, 0.375 , 0.59322034, 0.58333333],\n [0.38888889, 0.25 , 0.42372881, 0.375 ],\n [0.33333333, 0.16666667, 0.47457627, 0.41666667],\n [0.33333333, 0.16666667, 0.45762712, 0.375 ],\n [0.41666667, 0.29166667, 0.49152542, 0.45833333],\n [0.47222222, 0.29166667, 0.69491525, 0.625 ],\n [0.30555556, 0.41666667, 0.59322034, 0.58333333],\n [0.47222222, 0.58333333, 0.59322034, 0.625 ],\n [0.66666667, 0.45833333, 0.62711864, 0.58333333],\n [0.55555556, 0.125 , 0.57627119, 0.5 ],\n [0.36111111, 0.41666667, 0.52542373, 0.5 ],\n [0.33333333, 0.20833333, 0.50847458, 0.5 ],\n [0.33333333, 0.25 , 0.57627119, 0.45833333],\n [0.5 , 0.41666667, 0.61016949, 0.54166667],\n [0.41666667, 0.25 , 0.50847458, 0.45833333],\n [0.19444444, 0.125 , 0.38983051, 0.375 ],\n [0.36111111, 0.29166667, 0.54237288, 0.5 ],\n [0.38888889, 0.41666667, 0.54237288, 0.45833333],\n [0.38888889, 0.375 , 0.54237288, 0.5 ],\n [0.52777778, 0.375 , 0.55932203, 0.5 ],\n [0.22222222, 0.20833333, 0.33898305, 0.41666667],\n [0.38888889, 0.33333333, 0.52542373, 0.5 ],\n [0.55555556, 0.54166667, 0.84745763, 1. ],\n [0.41666667, 0.29166667, 0.69491525, 0.75 ],\n [0.77777778, 0.41666667, 0.83050847, 0.83333333],\n [0.55555556, 0.375 , 0.77966102, 0.70833333],\n [0.61111111, 0.41666667, 0.81355932, 0.875 ],\n [0.91666667, 0.41666667, 0.94915254, 0.83333333],\n [0.16666667, 0.20833333, 0.59322034, 0.66666667],\n [0.83333333, 0.375 , 0.89830508, 0.70833333],\n [0.66666667, 0.20833333, 0.81355932, 0.70833333],\n [0.80555556, 0.66666667, 0.86440678, 1. ],\n [0.61111111, 0.5 , 0.69491525, 0.79166667],\n [0.58333333, 0.29166667, 0.72881356, 0.75 ],\n [0.69444444, 0.41666667, 0.76271186, 0.83333333],\n [0.38888889, 0.20833333, 0.6779661 , 0.79166667],\n [0.41666667, 0.33333333, 0.69491525, 0.95833333],\n [0.58333333, 0.5 , 0.72881356, 0.91666667],\n [0.61111111, 0.41666667, 0.76271186, 0.70833333],\n [0.94444444, 0.75 , 0.96610169, 0.875 ],\n [0.94444444, 0.25 , 1. , 0.91666667],\n [0.47222222, 0.08333333, 0.6779661 , 0.58333333],\n [0.72222222, 0.5 , 0.79661017, 0.91666667],\n [0.36111111, 0.33333333, 0.66101695, 0.79166667],\n [0.94444444, 0.33333333, 0.96610169, 0.79166667],\n [0.55555556, 0.29166667, 0.66101695, 0.70833333],\n [0.66666667, 0.54166667, 0.79661017, 0.83333333],\n [0.80555556, 0.5 , 0.84745763, 0.70833333],\n [0.52777778, 0.33333333, 0.6440678 , 0.70833333],\n [0.5 , 0.41666667, 0.66101695, 0.70833333],\n [0.58333333, 0.33333333, 0.77966102, 0.83333333],\n [0.80555556, 0.41666667, 0.81355932, 0.625 ],\n [0.86111111, 0.33333333, 0.86440678, 0.75 ],\n [1. , 0.75 , 0.91525424, 0.79166667],\n [0.58333333, 0.33333333, 0.77966102, 0.875 ],\n [0.55555556, 0.33333333, 0.69491525, 0.58333333],\n [0.5 , 0.25 , 0.77966102, 0.54166667],\n [0.94444444, 0.41666667, 0.86440678, 0.91666667],\n [0.55555556, 0.58333333, 0.77966102, 0.95833333],\n [0.58333333, 0.45833333, 0.76271186, 0.70833333],\n [0.47222222, 0.41666667, 0.6440678 , 0.70833333],\n [0.72222222, 0.45833333, 0.74576271, 0.83333333],\n [0.66666667, 0.45833333, 0.77966102, 0.95833333],\n [0.72222222, 0.45833333, 0.69491525, 0.91666667],\n [0.41666667, 0.29166667, 0.69491525, 0.75 ],\n [0.69444444, 0.5 , 0.83050847, 0.91666667],\n [0.66666667, 0.54166667, 0.79661017, 1. ],\n [0.66666667, 0.41666667, 0.71186441, 0.91666667],\n [0.55555556, 0.20833333, 0.6779661 , 0.75 ],\n [0.61111111, 0.41666667, 0.71186441, 0.79166667],\n [0.52777778, 0.58333333, 0.74576271, 0.91666667],\n [0.44444444, 0.41666667, 0.69491525, 0.70833333]])<\/code><\/pre>\n1.4.3 \u8bad\u7ec3\u6a21\u578b<\/h2>\n
\u5bf9\u4e8e\u4e0d\u540c\u7684\u95ee\u9898\u9700\u8981\u8003\u8651\u4e0d\u540c\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5982\u5206\u7c7b\u95ee\u9898\u4f7f\u7528\u5206\u7c7b\u7b97\u6cd5\uff1b\u56de\u5f52\u95ee\u9898\u4f7f\u7528\u56de\u5f52\u7b97\u6cd5\u2026\u2026<\/p>\n
\u5bf9\u4e8e\u9e22\u5c3e\u82b1\u5206\u7c7b\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u5206\u7c7b\u95ee\u9898\uff0c\u4f46\u662f\u4f7f\u7528\u54ea\u4e2a\u5206\u7c7b\u7b97\u6cd5\u5462\uff1f\u6211\u4eec\u53ef\u4ee5\u4ecesklearn\u4f7f\u7528\u5730\u56fe\u4e2d\u83b7\u53d6\u3002<\/p>\n
\u9e22\u5c3e\u82b1\u7684\u6837\u672c\u6570\u5927\u4e8e50\u4e2a->\u5c5e\u4e8e\u5206\u7c7b\u95ee\u9898->\u6709\u5df2\u6807\u8bb0\u6570\u636e->\u6837\u672c\u6570\u5c0f\u4e8e100K->\u7ebf\u6027\u6838SVD(LinearSVC)<\/p>\n
from sklearn.model_selection import train_test_split\n\n# \u628a\u8bad\u7ec3\u96c6\u6309\u71677:3\u7684\u6bd4\u4f8b\u5206\u6210\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1\/3)\n'\u8bad\u7ec3\u96c6\u957f\u5ea6:{}'.format(len(y_train)), '\u6d4b\u8bd5\u96c6\u957f\u5ea6:{}'.format(len(y_test))<\/code><\/pre>\n('\u8bad\u7ec3\u96c6\u957f\u5ea6:100', '\u6d4b\u8bd5\u96c6\u957f\u5ea6:50')<\/code><\/pre>\ny_train<\/code><\/pre>\narray([1, 0, 0, 0, 2, 1, 1, 0, 2, 2, 2, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1,\n 1, 2, 0, 2, 0, 0, 2, 2, 2, 1, 0, 2, 0, 1, 2, 0, 1, 2, 1, 1, 0, 1,\n 1, 0, 1, 2, 2, 2, 0, 2, 2, 1, 2, 2, 1, 2, 0, 1, 0, 2, 0, 1, 1, 1,\n 0, 0, 1, 0, 2, 2, 0, 2, 0, 1, 1, 1, 1, 0, 1, 1, 2, 0, 0, 1, 1, 1,\n 2, 1, 2, 0, 2, 0, 1, 0, 1, 0, 0, 2])<\/code><\/pre>\ny_test<\/code><\/pre>\narray([1, 2, 1, 0, 0, 2, 1, 2, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 2, 1, 0, 2,\n 0, 1, 2, 1, 2, 2, 0, 2, 2, 2, 0, 1, 2, 2, 2, 1, 2, 0, 0, 0, 1, 0,\n 1, 2, 1, 0, 0, 0])<\/code><\/pre>\nfrom sklearn.svm import SVC\n# \u540c\u7406\nfrom sklearn.svm import LinearSVC\n\n# probability=Ture\u65f6\u624d\u80fd\u6253\u5370\u5206\u7c7b\u6982\u7387\uff0c\u5373\u624d\u80fd\u4f7f\u7528\u4e0b\u9762\u7684predict_proba()\u65b9\u6cd5\nclf = SVC(kernel='linear', probability=True)\n# \u8bad\u7ec3\u6570\u636e\nclf.fit(X_train, y_train)\n# \u9884\u6d4b\u6570\u636e\u5206\u7c7b\u7ed3\u679c\ny_prd = clf.predict(X_test)\ny_prd<\/code><\/pre>\narray([1, 2, 1, 0, 0, 2, 1, 2, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 2, 1, 0, 2,\n 0, 1, 2, 1, 2, 2, 0, 2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0,\n 1, 2, 1, 0, 0, 0])<\/code><\/pre>\ny_prd-y_test<\/code><\/pre>\narray([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, 1, 0, 0, 0, 0, 1, 0,\n 0, 0, 0, 0, 0, 0])<\/code><\/pre>\nclf.get_params()<\/code><\/pre>\n{'C': 1.0,\n 'cache_size': 200,\n 'class_weight': None,\n 'coef0': 0.0,\n 'decision_function_shape': 'ovr',\n 'degree': 3,\n 'gamma': 'auto_deprecated',\n 'kernel': 'linear',\n 'max_iter': -1,\n 'probability': True,\n 'random_state': None,\n 'shrinking': True,\n 'tol': 0.001,\n 'verbose': False}<\/code><\/pre>\nclf.C<\/code><\/pre>\n1.0<\/code><\/pre>\nclf.set_params(C=2)<\/code><\/pre>\nSVC(C=2, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n kernel='linear', max_iter=-1, probability=True, random_state=None,\n shrinking=True, tol=0.001, verbose=False)<\/code><\/pre>\nclf.get_params()<\/code><\/pre>\n{'C': 2,\n 'cache_size': 200,\n 'class_weight': None,\n 'coef0': 0.0,\n 'decision_function_shape': 'ovr',\n 'degree': 3,\n 'gamma': 'auto_deprecated',\n 'kernel': 'linear',\n 'max_iter': -1,\n 'probability': True,\n 'random_state': None,\n 'shrinking': True,\n 'tol': 0.001,\n 'verbose': False}<\/code><\/pre>\n# \u6253\u53701-5\u884c\u7684\u6240\u6709\u5217\nclf.predict_proba(X_test)[0:5, :]<\/code><\/pre>\narray([[0.02073772, 0.94985386, 0.02940841],\n [0.93450081, 0.04756914, 0.01793006],\n [0.00769491, 0.90027802, 0.09202706],\n [0.96549643, 0.02213395, 0.01236963],\n [0.01035414, 0.91467105, 0.07497481]])<\/code><\/pre>\n# \u67e5\u770b\u6a21\u578b\u5f97\u5206\uff0c\u6b64\u5904\u4e3a\u51c6\u786e\u7387\nclf.score(X_test, y_test)<\/code><\/pre>\n0.96<\/code><\/pre>\n1.4.4 \u6d4b\u8bd5\u6a21\u578b<\/h2>\n
\u6d4b\u8bd5\u6a21\u578b\u5219\u662f\u5728\u7b2c\u4e8c\u90e8\u5206\u8bf4\u7684\uff0c\u4f7f\u7528\u6a21\u578b\u6027\u80fd\u5ea6\u91cf\u5de5\u5177\u6d4b\u8bd5\u6a21\u578b\u7684\u6027\u80fd\u3002\u4e0a\u4e00\u8282\u7684score\u5176\u5b9e\u5c31\u662f\u4e00\u79cd\u5ea6\u91cf\u6a21\u578b\u6027\u80fd\u7684\u5de5\u5177\uff0c\u4f46\u662fscore\u53ea\u662f\u5bf9\u6a21\u578b\u505a\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u8bc4\u4f30\uff0c\u6211\u4eec\u901a\u5e38\u4f7f\u7528sklearn.metircs\u4e0b\u7684\u6a21\u5757\u5ea6\u91cf\u6a21\u578b\u6027\u80fd\uff1b\u4f7f\u7528sklearn.model_selection\u4e0b\u7684\u6a21\u5757\u8bc4\u4f30\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n
1.4.4.1 metircs\u6d4b\u8bd5\u6a21\u578b<\/h3>\nfrom sklearn.metrics import classification_report\n\nprint(classification_report(y, clf.predict(X), target_names=iris.target_names))<\/code><\/pre>\n precision recall f1-score support\n\n setosa 1.00 1.00 1.00 50\n versicolor 1.00 0.96 0.98 50\n virginica 0.96 1.00 0.98 50\n\n micro avg 0.99 0.99 0.99 150\n macro avg 0.99 0.99 0.99 150\nweighted avg 0.99 0.99 0.99 150<\/code><\/pre>\n1.4.4.2 k\u6298\u4ea4\u53c9\u9a8c\u8bc1<\/h3>\n
\u6b64\u5904\u4f7f\u7528k\u6298\u4ea4\u53c9\u9a8c\u8bc1\u5ea6\u91cf\u6a21\u578b\u6027\u80fd\u3002<\/p>\n
k\u6298\u4ea4\u53c9\u9a8c\u8bc1\uff1a<\/p>\n
\n- \u5c06\u6570\u636e\u968f\u673a\u7684\u5206\u4e3a?\u4e2a\u5b50\u96c6\uff08?\u7684\u53d6\u503c\u8303\u56f4\u4e00\u822c\u5728[1\u221220]\u4e4b\u95f4\uff09\uff0c\u7136\u540e\u53d6\u51fa?\u22121\u4e2a\u5b50\u96c6\u8fdb\u884c\u8bad\u7ec3\uff0c\u53e6\u4e00\u4e2a\u5b50\u96c6\u7528\u4f5c\u6d4b\u8bd5\u6a21\u578b\uff0c\u91cd\u590d?\u6b21\u8fd9\u4e2a\u8fc7\u7a0b\uff0c\u5f97\u5230\u6700\u4f18\u6a21\u578b\u3002<\/li>\n<\/ul>\n
\n- \u5c06\u6570\u636e\u5206\u4e3a$k$\u4e2a\u5b50\u96c6<\/li>\n
- \u9009\u62e9$k-1$\u4e2a\u5b50\u96c6\u8bad\u7ec3\u6a21\u578b<\/li>\n
- \u9009\u62e9\u53e6\u4e00\u4e2a\u5b50\u96c6\u6d4b\u8bd5\u6a21\u578b<\/li>\n
- \u91cd\u590d2-3\u6b65\uff0c\u76f4\u81f3\u6709$k$\u4e2a\u6a21\u578b<\/li>\n
- \u5bf9$k$\u4e2a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u53d6\u5e73\u5747\u503c<\/li>\n<\/ol>\n
\u4e0b\u56fe\u4e3a10\u6298\u4ea4\u53c9\u9a8c\u8bc1\u793a\u610f\u56fe\u3002<\/p>\n
[\u5916\u94fe\u56fe\u7247\u8f6c\u5b58\u5931\u8d25,\u6e90\u7ad9\u53ef\u80fd\u6709\u9632\u76d7\u94fe\u673a\u5236,\u5efa\u8bae\u5c06\u56fe\u7247\u4fdd\u5b58\u4e0b\u6765\u76f4\u63a5\u4e0a\u4f20(img-pTFy8dnD-1583320447319)(\u7b2c\u56db\u90e8\u5206-10\u6298\u4ea4\u53c9\u9a8c\u8bc1.jpg)]<\/p>\n
from sklearn.model_selection import cross_val_score\n\n# 10\u4e2a\u6a21\u578b\u7684\u5404\u81ea\u5f97\u5206\nscores = cross_val_score(clf, X, y, cv=10)\nscores<\/code><\/pre>\narray([1. , 1. , 1. , 1. , 0.86666667,\n 1. , 0.93333333, 1. , 1. , 1. ])<\/code><\/pre>\n# \u5e73\u5747\u5f97\u5206\u548c\u7f6e\u4fe1\u533a\u95f4\nprint('\u51c6\u786e\u7387:{:.4f}(+\/-{:.4f})'.format(scores.mean(), scores.std()*2))<\/code><\/pre>\n\u51c6\u786e\u7387:0.9800(+\/-0.0854)<\/code><\/pre>\n1.4.5 \u4f18\u5316\u6a21\u578b<\/h2>\n
\u8bad\u7ec3\u5e76\u6d4b\u8bd5\u6a21\u578b\u5df2\u7ecf\u8ba9\u6211\u4eec\u5f97\u5230\u4e86\u6700\u4f18\u7684\u53c2\u6570\uff0c\u4f18\u5316\u6a21\u578b\u5176\u5b9e\u76f8\u5f53\u4e8e\u627e\u51fa\u80fd\u591f\u4f7f\u5f97\u6a21\u578b\u6027\u80fd\u6700\u597d\u7684\u8d85\u53c2\u6570\uff0c\u4e5f\u53ef\u4ee5\u7406\u89e3\u6210\u6211\u4eec\u7684\u9a8c\u8bc1\u96c6\u7684\u4f5c\u7528\uff0c\u6b64\u5904\u6211\u4eec\u5c06\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u6cd5\u4f18\u5316\u6a21\u578b\uff0c\u5f97\u5230\u76f8\u5bf9\u6700\u597d\u7684\u4e00\u7ec4\u8d85\u53c2\u6570\u3002<\/p>\n
from sklearn.svm import SVC\nfrom sklearn.model_selection import GridSearchCV\n\n# \u6a21\u578b\nsvc = SVC()\n\n# \u8d85\u53c2\u6570\u5217\u8868\uff0c\u603b\u5171\u4f1a\u9a8c\u8bc14*4+4=20\u6b21\uff0c'linear'\u662f\u7ebf\u6027\u6838\uff0c\u7ebf\u6027\u6838\u8d85\u53c2\u6570\u6709\u4e00\u4e2a'C'\uff1brbf'\u662f\u9ad8\u65af\u6838\uff0c\u9ad8\u65af\u6838\u6709\u4e24\u4e2a\u8d85\u53c2\u6570'C'\u548c'gamma'\nparam_grid = [{'C': [0.1, 1, 10, 20], 'kernel':['linear']},\n {'C': [0.1, 1, 10, 20], 'kernel':['rbf'], 'gamma':[0.1, 1, 10, 20]}]\n\n# \u6253\u5206\u51fd\u6570\nscoring = 'accuracy'\n\nclf = GridSearchCV(estimator=svc, param_grid=param_grid,\n scoring=scoring, cv=10)\n\nclf = clf.fit(X, y)\nclf.predict(X)<\/code><\/pre>\narray([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, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 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])<\/code><\/pre>\nclf.get_params()<\/code><\/pre>\n{'cv': 10,\n 'error_score': 'raise-deprecating',\n 'estimator__C': 1.0,\n 'estimator__cache_size': 200,\n 'estimator__class_weight': None,\n 'estimator__coef0': 0.0,\n 'estimator__decision_function_shape': 'ovr',\n 'estimator__degree': 3,\n 'estimator__gamma': 'auto_deprecated',\n 'estimator__kernel': 'rbf',\n 'estimator__max_iter': -1,\n 'estimator__probability': False,\n 'estimator__random_state': None,\n 'estimator__shrinking': True,\n 'estimator__tol': 0.001,\n 'estimator__verbose': False,\n 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n kernel='rbf', max_iter=-1, probability=False, random_state=None,\n shrinking=True, tol=0.001, verbose=False),\n 'fit_params': None,\n 'iid': 'warn',\n 'n_jobs': None,\n 'param_grid': [{'C': [0.1, 1, 10, 20], 'kernel': ['linear']},\n {'C': [0.1, 1, 10, 20], 'kernel': ['rbf'], 'gamma': [0.1, 1, 10, 20]}],\n 'pre_dispatch': '2*n_jobs',\n 'refit': True,\n 'return_train_score': 'warn',\n 'scoring': 'accuracy',\n 'verbose': 0}<\/code><\/pre>\n# \u67e5\u770b\u6700\u4f18\u7684\u4e00\u7ec4\u8d85\u53c2\u6570\nclf.best_params_<\/code><\/pre>\n{'C': 10, 'kernel': 'linear'}<\/code><\/pre>\n# \u67e5\u770b\u6700\u4f18\u8d85\u53c2\u6570\u4e0b\u6a21\u578b\u7684\u51c6\u786e\u7387\nclf.best_score_<\/code><\/pre>\n0.98<\/code><\/pre>\n1.4.6 \u6301\u4e45\u5316\u6a21\u578b<\/h2>\n
\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u5f97\u5230\u7684\u6a21\u578b\u7684\u51c6\u786e\u7387\u67090.98\uff0c\u5df2\u7ecf\u662f\u6bd4\u8f83\u597d\u7684\u4e00\u4e2a\u6a21\u578b\u4e86\uff0c\u5f97\u5230\u8fd9\u4e2a\u6a21\u578b\u4e4b\u540e\uff0c\u6211\u4eec\u600e\u4e48\u6837\u624d\u80fd\u505a\u5230\u4e0b\u6b21\u518d\u4f7f\u7528\u5462\uff1f\u4e00\u822c\u4f1a\u901a\u8fc7\u6301\u4e45\u5316\u6a21\u578b\u7684\u65b9\u5f0f\u628a\u4e0a\u8ff0\u6a21\u578b\u4fdd\u5b58\u5230.plk\u6587\u4ef6\u4e2d\uff0c\u4e0b\u6b21\u4ece.plk\u6587\u4ef6\u4e2d\u53d6\u51fa\u76f4\u63a5\u4f7f\u7528\u5373\u53ef\uff0c\u901a\u5e38\u6301\u4e45\u5316\u7684\u65b9\u5f0f\u53ea\u6709\u4e24\u79cd\uff0c\u4e00\u79cd\u662f\u901a\u8fc7Python\u81ea\u5e26pickle\u5e93\uff0c\u53e6\u4e00\u79cd\u662f\u901a\u8fc7sklearn\u5e93\u4e0b\u7684joblib\u6a21\u5757\u3002<\/p>\n
1.4.6.1 pickle\u6a21\u5757<\/h3>\nimport pickle\n\n# \u4f7f\u7528pickle\u6a21\u5757\u628a\u6a21\u578b\u5e8f\u5217\u5316\u6210\u5b57\u7b26\u4e32\npkl_str = pickle.dumps(clf)\npkl_str[0:100]<\/code><\/pre>\nb'\\x80\\x03csklearn.model_selection._search\\nGridSearchCV\\nq\\x00)\\x81q\\x01}q\\x02(X\\x07\\x00\\x00\\x00scoringq\\x03X\\x08\\x00\\x00\\x00accuracyq\\x04X\\t\\x00\\x00\\x00estimato'<\/code><\/pre>\n# \u4f7f\u7528pickel\u6a21\u5757\u53cd\u5e8f\u5217\u5316\u5b57\u7b26\u4e32\u6210\u4e3a\u6a21\u578b\nclf2 = pickle.loads(pkl_str)\nclf2.predict(X)<\/code><\/pre>\narray([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, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 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])<\/code><\/pre>\n1.4.6.2 joblib\u6a21\u5757<\/h3>\nfrom sklearn.externals import joblib\n\n# \u4fdd\u5b58\u6a21\u578b\u5230clf.pkl\u6587\u4ef6\u5185\njoblib.dump(clf, 'clf.pkl')\n# \u4ececlf.pkl\u6587\u4ef6\u5185\u52a0\u8f7d\u6a21\u578b\nclf3 = joblib.load('clf.pkl')\nclf3.predict(X)<\/code><\/pre>\narray([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, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 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])<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u7ec6\u5206\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u7684\u6d41\u7a0b-\u6d41\u7a0b\u7b80\u4ecb 1.1 sklearn\u5b89\u88c5 \u4e3a\u4e86\u5b9e\u73b0\u63a5\u4e0b\u91cc\u7684 […]<\/p>\n","protected":false},"author":3,"featured_media":3275,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[276,301],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3270"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3270"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3270\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/media\/3275"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3270"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3270"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3270"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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