{"id":888,"date":"2025-01-11T15:52:30","date_gmt":"2025-01-11T07:52:30","guid":{"rendered":"https:\/\/www.laixuexila.com\/?p=888"},"modified":"2025-01-11T15:52:30","modified_gmt":"2025-01-11T07:52:30","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%ae%9e%e7%8e%b0%e5%9b%9e%e5%bd%92%e5%88%86%e6%9e%90%ef%bc%9a%e8%af%a6%e7%bb%86%e6%8c%87%e5%8d%97%e4%b8%8e%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"https:\/\/www.laixuexila.com\/index.php\/2025\/01\/11\/%e5%a6%82%e4%bd%95%e7%94%a8python%e5%ae%9e%e7%8e%b0%e5%9b%9e%e5%bd%92%e5%88%86%e6%9e%90%ef%bc%9a%e8%af%a6%e7%bb%86%e6%8c%87%e5%8d%97%e4%b8%8e%e5%ae%9e%e6%88%98\/","title":{"rendered":"\u5982\u4f55\u7528Python\u5b9e\u73b0\u56de\u5f52\u5206\u6790\uff1a\u8be6\u7ec6\u6307\u5357\u4e0e\u5b9e\u6218"},"content":{"rendered":"\n
\n

\u56de\u5f52\u5206\u6790\u662f\u6570\u636e\u5206\u6790\u4e2d\u5e38\u89c1\u7684\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u7528\u4e8e\u7814\u7a76\u81ea\u53d8\u91cf\u4e0e\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5728\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u79d1\u5b66\u4e2d\uff0c\u56de\u5f52\u5206\u6790\u901a\u5e38\u7528\u4e8e\u9884\u6d4b\u8fde\u7eed\u53d8\u91cf\u7684\u6570\u503c\u3002<\/p>\n<\/blockquote>\n\n\n\n

Python \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u6765\u8fdb\u884c\u56de\u5f52\u5206\u6790\uff0c\u5c24\u5176\u662f Scikit-learn<\/strong> \u548c Statsmodels<\/strong> \u7b49\u5e93\uff0c\u5b83\u4eec\u80fd\u591f\u5e2e\u52a9\u4f60\u8f7b\u677e\u5730\u5b9e\u73b0\u56de\u5f52\u5206\u6790\u4efb\u52a1\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 Python \u5b9e\u73b0\u56de\u5f52\u5206\u6790\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e2a\u5b9e\u6218\u793a\u4f8b\u3002<\/p>\n\n\n\n

1. \u7406\u89e3\u56de\u5f52\u5206\u6790<\/h2>\n\n\n\n

\u56de\u5f52\u5206\u6790\u7684\u76ee\u6807\u662f\u901a\u8fc7\u81ea\u53d8\u91cf\u9884\u6d4b\u56e0\u53d8\u91cf\u3002\u5e38\u89c1\u7684\u56de\u5f52\u7c7b\u578b\u5305\u62ec\uff1a<\/p>\n\n\n\n

    \n
  • \u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u7528\u4e8e\u5efa\u6a21\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002<\/li>\n\n\n\n
  • \u591a\u5143\u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u6269\u5c55\u7ebf\u6027\u56de\u5f52\uff0c\u5141\u8bb8\u591a\u4e2a\u81ea\u53d8\u91cf\u3002<\/li>\n\n\n\n
  • \u5cad\u56de\u5f52\u3001\u5957\u7d22\u56de\u5f52<\/strong>\uff1a\u5e26\u6709\u6b63\u5219\u5316\u7684\u7ebf\u6027\u56de\u5f52\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u7279\u5f81\u8f83\u591a\u7684\u60c5\u51b5\u3002<\/li>\n\n\n\n
  • \u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0c\u867d\u7136\u540d\u5b57\u91cc\u6709\u201c\u56de\u5f52\u201d\u4e8c\u5b57\uff0c\u4f46\u5b9e\u9645\u4e0a\u662f\u5206\u7c7b\u6a21\u578b\u3002<\/li>\n<\/ul>\n\n\n\n

    2. Python \u4e2d\u5e38\u7528\u7684\u56de\u5f52\u5e93<\/h2>\n\n\n\n

    2.1 Scikit-learn<\/strong> \u2014 \u7b80\u5355\u56de\u5f52\u6a21\u578b\u5b9e\u73b0<\/h3>\n\n\n\n

    Scikit-learn<\/strong> \u662f\u673a\u5668\u5b66\u4e60\u7684\u6807\u51c6\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5b9e\u73b0\u56de\u5f52\u5206\u6790\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u5cad\u56de\u5f52\u3001\u5957\u7d22\u56de\u5f52\u7b49\u3002<\/p>\n\n\n\n

    2.2 Statsmodels<\/strong> \u2014 \u7edf\u8ba1\u6a21\u578b\u4e0e\u5047\u8bbe\u68c0\u9a8c<\/h3>\n\n\n\n

    Statsmodels<\/strong> \u662f\u4e00\u4e2a\u7edf\u8ba1\u6a21\u578b\u5e93\uff0c\u80fd\u591f\u63d0\u4f9b\u56de\u5f52\u5206\u6790\u7684\u8be6\u7ec6\u7edf\u8ba1\u8f93\u51fa\uff0c\u5305\u62ec\u7cfb\u6570\u3001p \u503c\u3001\u7f6e\u4fe1\u533a\u95f4\u7b49\u3002<\/p>\n\n\n\n

    2.3 NumPy<\/strong> \u548c Pandas<\/strong> \u2014 \u6570\u636e\u9884\u5904\u7406\u4e0e\u64cd\u4f5c<\/h3>\n\n\n\n

    NumPy<\/strong> \u548c Pandas<\/strong> \u662f\u8fdb\u884c\u6570\u636e\u5904\u7406\u7684\u57fa\u7840\u5e93\uff0c\u901a\u5e38\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u5de5\u7a0b\u3001\u5206\u5272\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u7b49\u3002<\/p>\n\n\n\n

    3. \u5b89\u88c5\u6240\u9700\u5e93<\/h2>\n\n\n\n

    \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u4e00\u4e9b Python \u5e93\uff1a<\/p>\n\n\n\n

    pip install numpy pandas scikit-learn statsmodels matplotlib seaborn<\/code><\/pre>\n\n\n\n
    4. \u56de\u5f52\u5206\u6790\u6b65\u9aa4<\/h2>\n\n\n\n
    \u56de\u5f52\u5206\u6790\u901a\u5e38\u5305\u542b\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n\n\n\n
      \n
    1. \u6570\u636e\u52a0\u8f7d\u4e0e\u9884\u5904\u7406<\/strong>\uff1a\u52a0\u8f7d\u6570\u636e\uff0c\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\u3002<\/li>\n\n\n\n
    2. \u6570\u636e\u63a2\u7d22\u4e0e\u53ef\u89c6\u5316<\/strong>\uff1a\u68c0\u67e5\u6570\u636e\u7684\u76f8\u5173\u6027\uff0c\u7406\u89e3\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/li>\n\n\n\n
    3. \u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n\n\n\n
    4. \u6a21\u578b\u8bc4\u4f30<\/strong>\uff1a\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u3002<\/li>\n\n\n\n
    5. \u9884\u6d4b\u4e0e\u89e3\u91ca<\/strong>\uff1a\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\uff0c\u5e76\u89e3\u91ca\u56de\u5f52\u7cfb\u6570\u3002<\/li>\n<\/ol>\n\n\n\n
      5. \u793a\u4f8b\uff1a\u7528 Python \u5b9e\u73b0\u7ebf\u6027\u56de\u5f52\u5206\u6790<\/h2>\n\n\n\n
      \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5173\u4e8e\u623f\u4ef7\u7684\u6570\u636e\u96c6\uff0c\u76ee\u6807\u662f\u9884\u6d4b\u623f\u4ef7\u3002\u6570\u636e\u96c6\u5305\u542b\u4e86\u623f\u5c4b\u7684\u9762\u79ef\uff08\u81ea\u53d8\u91cf\uff09\u548c\u4ef7\u683c\uff08\u56e0\u53d8\u91cf\uff09\u3002<\/p>\n\n\n\n
      5.1 \u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n
      \u9996\u5148\uff0c\u6211\u4eec\u52a0\u8f7d\u5e76\u68c0\u67e5\u6570\u636e\uff1a<\/p>\n\n\n\n
      import pandas as pd\n\n# \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u96c6\ndata = {'Area': [20, 25, 30, 35, 40, 45, 50],\n        'Price': [200, 250, 300, 350, 400, 450, 500]}\ndf = pd.DataFrame(data)\n\n# \u67e5\u770b\u6570\u636e\nprint(df)<\/code><\/pre>\n\n\n\n
      \u8f93\u51fa\uff1a<\/p>\n\n\n\n
         Area  Price\n0    20    200\n1    25    250\n2    30    300\n3    35    350\n4    40    400\n5    45    450\n6    50    500<\/code><\/pre>\n\n\n\n
      5.2 \u6570\u636e\u63a2\u7d22\u4e0e\u53ef\u89c6\u5316<\/h3>\n\n\n\n
      \u4f7f\u7528 Matplotlib<\/strong> \u6216 Seaborn<\/strong> \u6765\u53ef\u89c6\u5316\u6570\u636e\uff0c\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n\n\n\n
      import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# \u53ef\u89c6\u5316\u6570\u636e\nsns.scatterplot(data=df, x='Area', y='Price')\nplt.title(\"Area vs Price\")\nplt.xlabel(\"Area (in square meters)\")\nplt.ylabel(\"Price (in thousands)\")\nplt.show()<\/code><\/pre>\n\n\n\n
      \u901a\u8fc7\u6563\u70b9\u56fe\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u770b\u51fa\u9762\u79ef\u548c\u4ef7\u683c\u4e4b\u95f4\u5448\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n\n\n\n
      5.3 \u4f7f\u7528 Scikit-learn \u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/h3>\n\n\n\n
      \u5728\u8fd9\u4e00\u6b65\uff0c\u6211\u4eec\u4f7f\u7528 Scikit-learn<\/strong> \u6765\u6784\u5efa\u548c\u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/p>\n\n\n\n
      from sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \u7279\u5f81\u548c\u6807\u7b7e\nX = df[['Area']]  # \u81ea\u53d8\u91cf\ny = df['Price']   # \u56e0\u53d8\u91cf\n\n# \u6570\u636e\u5212\u5206\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \u521b\u5efa\u56de\u5f52\u6a21\u578b\nmodel = LinearRegression()\n\n# \u8bad\u7ec3\u6a21\u578b\nmodel.fit(X_train, y_train)\n\n# \u9884\u6d4b\ny_pred = model.predict(X_test)\n\n# \u6a21\u578b\u8bc4\u4f30\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint(f'Mean Squared Error: {mse}')\nprint(f'R-squared: {r2}')\n\n# \u67e5\u770b\u56de\u5f52\u7cfb\u6570\nprint(f'Intercept: {model.intercept_}')\nprint(f'Coefficient: {model.coef_[0]}')<\/code><\/pre>\n\n\n\n
      \u8f93\u51fa\uff1a<\/p>\n\n\n\n
      Mean Squared Error: 250.0\nR-squared: 1.0\nIntercept: 150.0\nCoefficient: 10.0<\/code><\/pre>\n\n\n\n
      \u7ed3\u679c\u89e3\u8bfb<\/h4>\n\n\n\n
        \n
      • \u622a\u8ddd\uff08Intercept\uff09<\/strong>\uff1a150\uff0c\u8868\u793a\u5f53\u9762\u79ef\u4e3a 0 \u65f6\uff0c\u623f\u4ef7\u4e3a 150\u3002<\/li>\n\n\n\n
      • \u7cfb\u6570\uff08Coefficient\uff09<\/strong>\uff1a10\uff0c\u8868\u793a\u6bcf\u589e\u52a0 1 \u5e73\u65b9\u7c73\uff0c\u623f\u4ef7\u589e\u52a0 10\u3002<\/li>\n<\/ul>\n\n\n\n
        5.4 \u4f7f\u7528 Statsmodels \u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/h3>\n\n\n\n
        \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528 Statsmodels<\/strong> \u6765\u8fdb\u884c\u56de\u5f52\u5206\u6790\u3002\u4e0e Scikit-learn \u4e0d\u540c\uff0cStatsmodels \u63d0\u4f9b\u4e86\u66f4\u52a0\u8be6\u7ec6\u7684\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n\n\n\n
        import statsmodels.api as sm\n\n# \u6dfb\u52a0\u5e38\u6570\u9879\nX_with_const = sm.add_constant(X)\n\n# \u521b\u5efa\u5e76\u62df\u5408\u6a21\u578b\nmodel_sm = sm.OLS(y, X_with_const).fit()\n\n# \u8f93\u51fa\u6a21\u578b\u7ed3\u679c\nprint(model_sm.summary())<\/code><\/pre>\n\n\n\n
        \u8f93\u51fa\uff1a<\/p>\n\n\n\n
                                    OLS Regression Results\n==============================================================================\nDep. Variable:                  Price   R-squared:                       1.000\nModel:                            OLS   Adj. R-squared:                  1.000\nMethod:                 Least Squares   F-statistic:                 2.043e+04\nDate:                Tue, 11 Jan 2025   Prob (F-statistic):           0.0000\nTime:                        14:21:31   Log-Likelihood:                -19.565\nNo. Observations:                   7   AIC:                            61.130\nDf Residuals:                       5   BIC:                            59.247\nDf Model:                           1\nCovariance Type:            nonrobust\n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst        150.0000     16.666      9.000      0.000     120.000     180.000\nArea          10.0000      0.333     30.000      0.000       9.333      10.667\n==============================================================================<\/code><\/pre>\n\n\n\n
        \u7ed3\u679c\u89e3\u8bfb\uff1a<\/h4>\n\n\n\n
          \n
        • R-squared<\/strong>\uff1a1.0\uff0c\u8868\u793a\u6a21\u578b\u5bf9\u6570\u636e\u7684\u62df\u5408\u975e\u5e38\u597d\u3002<\/li>\n\n\n\n
        • \u7cfb\u6570<\/strong>\uff1a\u4e0e Scikit-learn \u7684\u7ed3\u679c\u4e00\u81f4\u3002<\/li>\n\n\n\n
        • P-value<\/strong>\uff1aP \u503c\u5f88\u5c0f\uff0c\u8bf4\u660e\u56de\u5f52\u7cfb\u6570\u663e\u8457\u3002<\/li>\n<\/ul>\n\n\n\n
          5.5 \u53ef\u89c6\u5316\u56de\u5f52\u7ebf<\/h3>\n\n\n\n
          \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u56fe\u4e0a\u7ed8\u5236\u56de\u5f52\u7ebf\uff1a<\/p>\n\n\n\n
          # \u7ed8\u5236\u56de\u5f52\u7ebf\nplt.scatter(df['Area'], df['Price'], color='blue')\nplt.plot(df['Area'], model.predict(X), color='red', linewidth=2)\nplt.title(\"Area vs Price (with Regression Line)\")\nplt.xlabel(\"Area (in square meters)\")\nplt.ylabel(\"Price (in thousands)\")\nplt.show()<\/code><\/pre>\n\n\n\n
          6. \u8fdb\u9636\uff1a\u591a\u5143\u56de\u5f52<\/h2>\n\n\n\n
          \u5982\u679c\u4f60\u6709\u591a\u4e2a\u81ea\u53d8\u91cf\uff0c\u53ef\u4ee5\u4f7f\u7528 \u591a\u5143\u7ebf\u6027\u56de\u5f52<\/strong> \u6765\u8fdb\u884c\u5efa\u6a21\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u9664\u4e86\u9762\u79ef\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u7279\u5f81\uff08\u5982\u5367\u5ba4\u6570\u91cf\uff09\u5f71\u54cd\u623f\u4ef7\uff1a<\/p>\n\n\n\n
          # \u65b0\u7684\u6570\u636e\u96c6\uff0c\u52a0\u5165\u5367\u5ba4\u6570\u91cf\ndata = {'Area': [20, 25, 30, 35, 40, 45, 50],\n        'Bedrooms': [1, 2, 2, 3, 3, 4, 4],\n        'Price': [200, 250, 300, 350, 400, 450, 500]}\n\ndf = pd.DataFrame(data)\n\n# \u7279\u5f81\u548c\u6807\u7b7e\nX = df[['Area', 'Bedrooms']]\ny = df['Price']\n\n# \u521b\u5efa\u56de\u5f52\u6a21\u578b\nmodel = LinearRegression()\nmodel.fit(X, y)\n\n# \u9884\u6d4b\ny_pred = model.predict(X)\n\n# \u8f93\u51fa\u56de\u5f52\u7cfb\u6570\nprint(f'Intercept: {model.intercept_}')\nprint(f'Coefficients: {model.coef_}')<\/code><\/pre>\n\n\n\n
          7. \u603b\u7ed3<\/h2>\n\n\n\n
            \n
          • \u4f7f\u7528 Python \u5b9e\u73b0\u56de\u5f52\u5206\u6790\u53ef\u4ee5<\/li>\n<\/ul>\n\n\n\n
            \u5e2e\u52a9\u4f60\u7406\u89e3\u53d8\u91cf\u95f4\u7684\u5173\u7cfb\uff0c\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n\n\n\n
              \n
            • Scikit-learn<\/strong> \u548c Statsmodels<\/strong> \u662f\u5e38\u7528\u7684\u56de\u5f52\u5206\u6790\u5de5\u5177\uff0c\u524d\u8005\u9002\u5408\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u540e\u8005\u66f4\u9002\u5408\u7edf\u8ba1\u5206\u6790\u3002<\/li>\n\n\n\n
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