{"id":20114,"date":"2026-01-21T09:26:59","date_gmt":"2026-01-21T01:26:59","guid":{"rendered":"https:\/\/92it.top\/?p=20114"},"modified":"2026-01-21T09:26:59","modified_gmt":"2026-01-21T01:26:59","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0_%e8%bf%87%e6%8b%9f%e5%90%88%e3%80%81%e6%ac%a0%e6%8b%9f%e5%90%88%e3%80%81%e5%81%8f%e5%b7%ae%e4%b8%8e%e6%96%b9%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/92it.top\/?p=20114","title":{"rendered":"\u673a\u5668\u5b66\u4e60_\u8fc7\u62df\u5408\u3001\u6b20\u62df\u5408\u3001\u504f\u5dee\u4e0e\u65b9\u5dee"},"content":{"rendered":"\n<p><strong>\u524d\u8a00\u200b\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u5728\u673a\u5668\u5b66\u4e60\u7684\u4e16\u754c\u91cc\uff0c\u6784\u5efa\u4e00\u4e2a\u6a21\u578b\u5c31\u50cf\u8bad\u7ec3\u4e00\u4f4d\u5b66\u751f\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u5e0c\u671b\u8fd9\u4f4d<strong>\u5b66\u751f<\/strong>\u4e0d\u4ec5\u80fd\u8bb0\u4f4f\u8bfe\u672c\u4e0a\u7684\u4f8b\u9898\uff08\u8bad\u7ec3\u6570\u636e\uff09\uff0c\u66f4\u80fd\u6df1\u523b\u7406\u89e3\u80cc\u540e\u7684\u539f\u7406\uff0c\u4ece\u800c\u5728\u5168\u65b0\u7684\u3001\u4ece\u672a\u89c1\u8fc7\u7684\u8003\u9898\uff08\u6d4b\u8bd5\u6570\u636e\uff09\u4e0a\u4e5f\u80fd\u53d6\u5f97\u597d\u6210\u7ee9\u3002\u7136\u800c\uff0c\u8fd9\u4f4d<strong>\u5b66\u751f<\/strong>\u5728\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u4f1a\u9047\u5230\u4e24\u79cd\u5178\u578b\u95ee\u9898\uff1a<\/p>\n\n\n\n<ul>\n<li>\u4e00\u79cd\u662f\u5b66\u5f97\u592a\u6b7b\u677f\uff0c\u53ea\u4f1a\u751f\u642c\u786c\u5957\u4f8b\u9898\uff08<strong>\u6b20\u62df\u5408<\/strong>\uff09\uff1b<\/li>\n\n\n\n<li>\u53e6\u4e00\u79cd\u662f\u5b66\u5f97\u592a\u806a\u660e\uff0c\u628a\u4f8b\u9898\u7684\u6807\u70b9\u7b26\u53f7\u751a\u81f3\u7b14\u8ff9\u7279\u70b9\u90fd\u80cc\u4e0b\u6765\u4e86\uff0c\u5bfc\u81f4\u9762\u5bf9\u65b0\u9898\u65f6\u4e0d\u77e5\u6240\u63aa\uff08<strong>\u8fc7\u62df\u5408<\/strong>\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u7406\u89e3&nbsp;<strong>\u8fc7\u62df\u5408<\/strong>&nbsp;\u4e0e&nbsp;<strong>\u6b20\u62df\u5408<\/strong>\uff0c\u4ee5\u53ca\u5176\u80cc\u540e\u66f4\u6df1\u5c42\u7684\u7406\u8bba\u6982\u5ff5\u2014\u2014<strong>\u504f\u5dee<\/strong>&nbsp;\u4e0e&nbsp;<strong>\u65b9\u5dee<\/strong>\uff0c\u662f\u6bcf\u4e00\u4f4d\u673a\u5668\u5b66\u4e60\u5b9e\u8df5\u8005\u4ece\u5165\u95e8\u8d70\u5411\u7cbe\u901a\u7684\u5173\u952e\u4e00\u6b65\u3002\u5b83\u4eec\u89e3\u91ca\u4e86\u6a21\u578b\u4e3a\u4f55\u4f1a\u72af\u9519\uff0c\u5e76\u4e3a\u6211\u4eec\u6307\u660e\u4e86\u6a21\u578b\u6539\u8fdb\u7684\u65b9\u5411\u3002<\/p>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u6838\u5fc3\u6982\u5ff5\uff1a\u6a21\u578b\u7684\u8868\u73b0\u4e0e&#8221;\u62df\u5408&#8221;\u72b6\u6001\u200b\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u76f4\u89c2\u7684\u4f8b\u5b50\u6765\u7406\u89e3\u4ec0\u4e48\u662f<strong>\u62df\u5408<\/strong>\u3002\u5047\u8bbe\u6211\u4eec\u60f3\u7528\u4e00\u4e2a\u6570\u5b66\u6a21\u578b\u6765\u62df\u5408\u4e00\u7ec4\u6563\u70b9\u6570\u636e\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport matplotlib.pyplot as plt\n\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 start --------------------------\nplt.rcParams['font.sans-serif'] = [\n    # Windows \u4f18\u5148\n    'SimHei', 'Microsoft YaHei',\n    # macOS \u4f18\u5148\n    'PingFang SC', 'Heiti TC',\n    # Linux \u4f18\u5148\n    'WenQuanYi Micro Hei', 'DejaVu Sans'\n]\n# \u4fee\u590d\u8d1f\u53f7\u663e\u793a\u4e3a\u65b9\u5757\u7684\u95ee\u9898\nplt.rcParams['axes.unicode_minus'] = False\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 end --------------------------\n\n# \u751f\u6210\u6a21\u62df\u6570\u636e\uff1a\u5728\u6b63\u5f26\u66f2\u7ebf\u57fa\u7840\u4e0a\u52a0\u5165\u4e00\u4e9b\u968f\u673a\u566a\u58f0\nnp.random.seed(42)\nX = np.linspace(0, 10, 20)\ny_true = np.sin(X)                     # \u771f\u5b9e\u7684\u6f5c\u5728\u89c4\u5f8b\uff08\u6211\u4eec\u4e0d\u77e5\u9053\uff09\ny_noise = np.random.randn(20) * 0.3   # \u968f\u673a\u566a\u58f0\ny = y_true + y_noise                  # \u6211\u4eec\u5b9e\u9645\u89c2\u6d4b\u5230\u7684\u6570\u636e\n\nplt.scatter(X, y, label='\u89c2\u6d4b\u6570\u636e (\u542b\u566a\u58f0)', color='blue', alpha=0.6)\nplt.plot(X, y_true, label='\u771f\u5b9e\u89c4\u5f8b (y=sin(x))', color='green', linewidth=2)\nplt.xlabel('X')\nplt.ylabel('y')\nplt.title('\u6570\u636e\u4e0e\u6f5c\u5728\u89c4\u5f8b')\nplt.legend()\nplt.grid(True)\nplt.show()<\/pre>\n\n\n\n<p>\u6211\u4eec\u7684\u76ee\u6807\u662f\u627e\u5230\u4e00\u6761\u66f2\u7ebf\uff08\u6a21\u578b\uff09\uff0c\u80fd\u6700\u597d\u5730\u63cf\u8ff0\u8fd9\u4e9b\u84dd\u8272\u6563\u70b9\uff08\u6570\u636e\uff09\u6240\u53cd\u6620\u7684\u89c4\u5f8b\u3002<\/p>\n\n\n\n<p>\u6a21\u578b\u5bf9\u6570\u636e\u7684\u63cf\u8ff0\u7a0b\u5ea6\uff0c\u5c31\u662f<strong>\u62df\u5408<\/strong>\u3002<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"886\" height=\"630\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73.png\" alt=\"\" class=\"wp-image-20115\" style=\"width:446px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73.png 886w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-300x213.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-768x546.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-830x590.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-230x164.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-350x249.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-73-480x341.png 480w\" sizes=\"(max-width: 886px) 100vw, 886px\" \/><\/figure><\/div>\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd391. \u6b20\u62df\u5408<\/strong><\/p>\n\n\n\n<p><strong>\u6b20\u62df\u5408<\/strong>&nbsp;\u662f\u6307\u6a21\u578b\u8fc7\u4e8e\u7b80\u5355\uff0c\u65e0\u6cd5\u6355\u6349\u6570\u636e\u4e2d\u7684\u57fa\u672c\u89c4\u5f8b\u6216\u6a21\u5f0f\u3002\u5c31\u50cf\u4e00\u4e2a\u5b66\u751f\u53ea\u5b66\u4e86\u52a0\u6cd5\uff0c\u5374\u8981\u53bb\u89e3\u5fae\u79ef\u5206\u9898\u76ee\u3002<\/p>\n\n\n\n<ul>\n<li><strong>\u8868\u73b0<\/strong>\uff1a\u6a21\u578b\u5728<strong>\u8bad\u7ec3\u6570\u636e<\/strong>\u4e0a\u8868\u73b0\u5c31\u5f88\u5dee\uff08\u4f8b\u5982\uff0c\u51c6\u786e\u7387\u4f4e\uff0c\u8bef\u5dee\u5927\uff09\u3002<\/li>\n\n\n\n<li><strong>\u539f\u56e0<\/strong>\uff1a\u6a21\u578b\u590d\u6742\u5ea6\u592a\u4f4e\uff0c\u7279\u5f81\u4e0d\u8db3\uff0c\u6216\u8bad\u7ec3\u4e0d\u5145\u5206\u3002<\/li>\n\n\n\n<li><strong>\u7c7b\u6bd4<\/strong>\uff1a\u7528\u4e00\u6761\u76f4\u7ebf\uff08\u4e00\u6b21\u591a\u9879\u5f0f\uff09\u53bb\u62df\u5408\u6709\u660e\u663e\u5f2f\u66f2\u8d8b\u52bf\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.metrics import mean_squared_error\n\n# \u5c1d\u8bd5\u75281\u9636\u591a\u9879\u5f0f\uff08\u76f4\u7ebf\uff09\u62df\u5408\npoly = PolynomialFeatures(degree=1)\nX_poly1 = poly.fit_transform(X.reshape(-1, 1))\nmodel_under = LinearRegression()\nmodel_under.fit(X_poly1, y)\ny_pred_under = model_under.predict(X_poly1)\n\nmse_train_under = mean_squared_error(y, y_pred_under)\nprint(f\"\u6b20\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): {mse_train_under:.4f}\")<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\u6b20\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): 0.4402\n<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd392. \u6070\u5230\u597d\u5904\u7684\u62df\u5408<\/strong><\/p>\n\n\n\n<p>\u8fd9\u662f\u7406\u60f3\u72b6\u6001\u3002\u6a21\u578b\u8db3\u591f\u590d\u6742\u4ee5\u5b66\u4e60\u6570\u636e\u4e2d\u7684\u5173\u952e\u6a21\u5f0f\uff0c\u4f46\u53c8\u4e0d\u4f1a\u590d\u6742\u5230\u53bb\u5b66\u4e60\u968f\u673a\u566a\u58f0\u3002\u5b83\u80fd\u5728\u8bad\u7ec3\u96c6\u548c\u672a\u77e5\u7684\u6d4b\u8bd5\u96c6\u4e0a\u90fd\u8868\u73b0\u826f\u597d\u3002<\/p>\n\n\n\n<ul>\n<li><strong>\u8868\u73b0<\/strong>\uff1a\u5728\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8bef\u5dee\u90fd\u8f83\u4f4e\uff0c\u4e14\u4e24\u8005\u63a5\u8fd1\u3002<\/li>\n\n\n\n<li><strong>\u7c7b\u6bd4<\/strong>\uff1a\u7528\u4e00\u4e2a\u9002\u5f53\u9636\u6570\u7684\u591a\u9879\u5f0f\uff08\u4f8b\u59823\u9636\uff09\u6765\u62df\u5408\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u5c1d\u8bd5\u75283\u9636\u591a\u9879\u5f0f\u62df\u5408\npoly = PolynomialFeatures(degree=3)\nX_poly3 = poly.fit_transform(X.reshape(-1, 1))\nmodel_good = LinearRegression()\nmodel_good.fit(X_poly3, y)\ny_pred_good = model_good.predict(X_poly3)\n\nmse_train_good = mean_squared_error(y, y_pred_good)\nprint(f\"\u826f\u597d\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): {mse_train_good:.4f}\")<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\u826f\u597d\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): 0.3988\n<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd393. \u8fc7\u62df\u5408<\/strong><\/p>\n\n\n\n<p><strong>\u8fc7\u62df\u5408<\/strong>&nbsp;\u662f\u6307\u6a21\u578b\u8fc7\u4e8e\u590d\u6742\uff0c\u4e0d\u4ec5\u5b66\u4e60\u4e86\u6570\u636e\u4e2d\u7684\u771f\u5b9e\u89c4\u5f8b\uff0c\u8fd8&#8221;\u8bb0\u4f4f&#8221;\u4e86\u8bad\u7ec3\u6570\u636e\u4e2d\u7684\u968f\u673a\u566a\u58f0\u548c\u5f02\u5e38\u503c\u3002<\/p>\n\n\n\n<ul>\n<li><strong>\u8868\u73b0<\/strong>\uff1a\u6a21\u578b\u5728<strong>\u8bad\u7ec3\u6570\u636e<\/strong>\u4e0a\u8868\u73b0\u6781\u597d\uff08\u8bef\u5dee\u6781\u5c0f\uff09\uff0c\u4f46\u5728<strong>\u65b0\u7684\u3001\u672a\u89c1\u8fc7\u7684\u6570\u636e<\/strong>\u4e0a\u8868\u73b0\u6025\u5267\u4e0b\u964d\uff0c\u6cdb\u5316\u80fd\u529b\u5dee\u3002<\/li>\n\n\n\n<li><strong>\u539f\u56e0<\/strong>\uff1a\u6a21\u578b\u590d\u6742\u5ea6\u8fc7\u9ad8\uff0c\u8bad\u7ec3\u6570\u636e\u91cf\u592a\u5c11\u3002<\/li>\n\n\n\n<li><strong>\u7c7b\u6bd4<\/strong>\uff1a\u7528\u4e00\u4e2a\u975e\u5e38\u9ad8\u9636\u7684\u591a\u9879\u5f0f\uff08\u4f8b\u598215\u9636\uff09\u53bb\u62df\u5408\u6570\u636e\uff0c\u4f7f\u5f97\u66f2\u7ebf\u7a7f\u8fc7\u4e86\u51e0\u4e4e\u6bcf\u4e00\u4e2a\u6570\u636e\u70b9\uff0c\u53d8\u5f97\u6781\u5ea6\u626d\u66f2\u3002<\/li>\n<\/ul>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.metrics import mean_squared_error\n\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 start --------------------------\nplt.rcParams['font.sans-serif'] = [\n    # Windows \u4f18\u5148\n    'SimHei', 'Microsoft YaHei',\n    # macOS \u4f18\u5148\n    'PingFang SC', 'Heiti TC',\n    # Linux \u4f18\u5148\n    'WenQuanYi Micro Hei', 'DejaVu Sans'\n]\n# \u4fee\u590d\u8d1f\u53f7\u663e\u793a\u4e3a\u65b9\u5757\u7684\u95ee\u9898\nplt.rcParams['axes.unicode_minus'] = False\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 end --------------------------\n\n# \u751f\u6210\u6a21\u62df\u6570\u636e\uff1a\u5728\u6b63\u5f26\u66f2\u7ebf\u57fa\u7840\u4e0a\u52a0\u5165\u4e00\u4e9b\u968f\u673a\u566a\u58f0\nnp.random.seed(42)\nX = np.linspace(0, 10, 20)\ny_true = np.sin(X)                     # \u771f\u5b9e\u7684\u6f5c\u5728\u89c4\u5f8b\uff08\u6211\u4eec\u4e0d\u77e5\u9053\uff09\ny_noise = np.random.randn(20) * 0.3   # \u968f\u673a\u566a\u58f0\ny = y_true + y_noise                  # \u6211\u4eec\u5b9e\u9645\u89c2\u6d4b\u5230\u7684\u6570\u636e\n\n# \u5c1d\u8bd5\u75281\u9636\u591a\u9879\u5f0f\uff08\u76f4\u7ebf\uff09\u62df\u5408\npoly = PolynomialFeatures(degree=1)\nX_poly1 = poly.fit_transform(X.reshape(-1, 1))\nmodel_under = LinearRegression()\nmodel_under.fit(X_poly1, y)\ny_pred_under = model_under.predict(X_poly1)\n\nmse_train_under = mean_squared_error(y, y_pred_under)\nprint(f\"\u6b20\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): {mse_train_under:.4f}\")\n\n# \u5c1d\u8bd5\u75283\u9636\u591a\u9879\u5f0f\u62df\u5408\npoly = PolynomialFeatures(degree=3)\nX_poly3 = poly.fit_transform(X.reshape(-1, 1))\nmodel_good = LinearRegression()\nmodel_good.fit(X_poly3, y)\ny_pred_good = model_good.predict(X_poly3)\n\nmse_train_good = mean_squared_error(y, y_pred_good)\nprint(f\"\u826f\u597d\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): {mse_train_good:.4f}\")\n\n# \u5c1d\u8bd5\u752815\u9636\u591a\u9879\u5f0f\u62df\u5408\uff08\u6781\u6613\u8fc7\u62df\u5408\uff09\npoly = PolynomialFeatures(degree=15)\nX_poly15 = poly.fit_transform(X.reshape(-1, 1))\nmodel_over = LinearRegression()\nmodel_over.fit(X_poly15, y)\ny_pred_over = model_over.predict(X_poly15)\n\nmse_train_over = mean_squared_error(y, y_pred_over)\nprint(f\"\u8fc7\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): {mse_train_over:.4f}\")\n\n# \u53ef\u89c6\u5316\u4e09\u79cd\u62df\u5408\u72b6\u6001\nplt.figure(figsize=(15, 4))\n\n# \u6b20\u62df\u5408\nplt.subplot(1, 3, 1)\nplt.scatter(X, y, alpha=0.6)\nplt.plot(X, y_pred_under, color='red', linewidth=2, label='\u6b20\u62df\u5408 (1\u9636)')\nplt.plot(X, y_true, color='green', linestyle='--', label='\u771f\u5b9e\u89c4\u5f8b')\nplt.title(f'\u6b20\u62df\u5408\\n\u8bad\u7ec3MSE: {mse_train_under:.4f}')\nplt.legend()\nplt.grid(True)\n\n# \u826f\u597d\u62df\u5408\nplt.subplot(1, 3, 2)\nplt.scatter(X, y, alpha=0.6)\nplt.plot(X, y_pred_good, color='red', linewidth=2, label='\u826f\u597d\u62df\u5408 (3\u9636)')\nplt.plot(X, y_true, color='green', linestyle='--', label='\u771f\u5b9e\u89c4\u5f8b')\nplt.title(f'\u826f\u597d\u62df\u5408\\n\u8bad\u7ec3MSE: {mse_train_good:.4f}')\nplt.legend()\nplt.grid(True)\n\n# \u8fc7\u62df\u5408\nplt.subplot(1, 3, 3)\nplt.scatter(X, y, alpha=0.6)\nplt.plot(X, y_pred_over, color='red', linewidth=2, label='\u8fc7\u62df\u5408 (15\u9636)')\nplt.plot(X, y_true, color='green', linestyle='--', label='\u771f\u5b9e\u89c4\u5f8b')\nplt.title(f'\u8fc7\u62df\u5408\\n\u8bad\u7ec3MSE: {mse_train_over:.4f}')\nplt.legend()\nplt.grid(True)\n\nplt.tight_layout()\nplt.show()<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\u6b20\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): 0.4402\n\u826f\u597d\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): 0.3988\n\u8fc7\u62df\u5408\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5747\u65b9\u8bef\u5dee (MSE): 0.0650<\/pre>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"273\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-1024x273.png\" alt=\"\" class=\"wp-image-20116\" style=\"width:600px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-1024x273.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-300x80.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-768x205.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-1536x410.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-830x221.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-230x61.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-350x93.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74-480x128.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-74.png 1627w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p>\u4ece\u56fe\u4e2d\u53ef\u4ee5\u6e05\u6670\u770b\u5230\uff1a<\/p>\n\n\n\n<ul>\n<li><strong>\u6b20\u62df\u5408\uff08\u5de6\uff09<\/strong>\uff1a\u7ea2\u8272\u76f4\u7ebf\u5b8c\u5168\u65e0\u6cd5\u6355\u6349\u6570\u636e\u7684\u6ce2\u52a8\u8d8b\u52bf\u3002<\/li>\n\n\n\n<li><strong>\u826f\u597d\u62df\u5408\uff08\u4e2d\uff09<\/strong>\uff1a\u7ea2\u8272\u66f2\u7ebf\u5927\u81f4\u9075\u5faa\u4e86\u7eff\u8272\u771f\u5b9e\u89c4\u5f8b\u7684\u8d8b\u52bf\u3002<\/li>\n\n\n\n<li><strong>\u8fc7\u62df\u5408\uff08\u53f3\uff09<\/strong>\uff1a\u7ea2\u8272\u66f2\u7ebf\u5267\u70c8\u6ce2\u52a8\uff0c\u8bd5\u56fe\u7a7f\u8fc7\u6bcf\u4e00\u4e2a\u84dd\u8272\u6563\u70b9\uff0c\u5305\u62ec\u566a\u58f0\u70b9\uff0c\u5b8c\u5168\u5931\u53bb\u4e86\u6b63\u5f26\u66f2\u7ebf\u7684\u5149\u6ed1\u5f62\u6001\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u7406\u8bba\u57fa\u77f3\uff1a\u504f\u5dee\u4e0e\u65b9\u5dee\u5206\u89e3\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u504f\u5dee\u548c\u65b9\u5dee\u4e3a\u6211\u4eec\u7406\u89e3\u8fc7\u62df\u5408\u4e0e\u6b20\u62df\u5408\u63d0\u4f9b\u4e86\u7406\u8bba\u6846\u67b6\u3002\u5b83\u4eec\u63cf\u8ff0\u4e86\u6a21\u578b\u8bef\u5dee\u7684\u4e24\u4e2a\u4e0d\u540c\u6765\u6e90\u3002<\/p>\n\n\n\n<p>\u6211\u4eec\u53ef\u4ee5\u5c06\u6a21\u578b\u7684<strong>\u603b\u8bef\u5dee<\/strong>\u5206\u89e3\u4e3a\uff1a<strong>\u504f\u5dee\u00b2 + \u65b9\u5dee + \u4e0d\u53ef\u51cf\u5c11\u7684\u8bef\u5dee<\/strong>\u3002<\/p>\n\n\n\n<p><strong>\ud83d\udd391. \u504f\u5dee<\/strong><\/p>\n\n\n\n<ul>\n<li><strong>\u5b9a\u4e49<\/strong>\uff1a\u6a21\u578b\u9884\u6d4b\u503c\u7684<strong>\u671f\u671b<\/strong>\uff08\u5373\u5e73\u5747\u9884\u6d4b\u503c\uff09\u4e0e\u771f\u5b9e\u503c\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002\u53cd\u6620\u4e86\u6a21\u578b\u672c\u8eab\u7684<strong>\u7cfb\u7edf\u6027\u9519\u8bef<\/strong>\uff0c\u5373\u6a21\u578b\u5bf9\u95ee\u9898\u672c\u8d28\u7684\u5047\u8bbe\u662f\u5426\u6709\u8bef\u3002<\/li>\n\n\n\n<li><strong>\u9ad8\u504f\u5dee\u7684\u8868\u73b0<\/strong>\uff1a\u6a21\u578b\u8fc7\u4e8e\u7b80\u5355\uff0c\u65e0\u6cd5\u523b\u753b\u6570\u636e\u7279\u5f81\uff0c\u5bfc\u81f4<strong>\u6b20\u62df\u5408<\/strong>\u3002\u65e0\u8bba\u7528\u4ec0\u4e48\u6570\u636e\u8bad\u7ec3\uff0c\u7ed3\u679c\u90fd\u504f\u79bb\u771f\u5b9e\u503c\u3002<\/li>\n\n\n\n<li><strong>\u4f8b\u5b50<\/strong>\uff1a\u59cb\u7ec8\u7528&#8221;\u623f\u4ef7=\u9762\u79ef\u00d71000&#8243;\u8fd9\u4e2a\u7b80\u5355\u7ebf\u6027\u6a21\u578b\u6765\u9884\u6d4b\u5404\u79cd\u623f\u5b50\uff0c\u5ffd\u7565\u4e86\u5730\u6bb5\u3001\u697c\u5c42\u7b49\u91cd\u8981\u56e0\u7d20\uff0c\u8fd9\u5c31\u662f\u9ad8\u504f\u5dee\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd392. \u65b9\u5dee<\/strong><\/p>\n\n\n\n<ul>\n<li><strong>\u5b9a\u4e49<\/strong>\uff1a\u6a21\u578b\u9884\u6d4b\u503c\u81ea\u8eab\u7684<strong>\u6ce2\u52a8\u8303\u56f4<\/strong>\u3002\u53cd\u6620\u4e86\u6a21\u578b\u5bf9\u8bad\u7ec3\u6570\u636e\u4e2d<strong>\u968f\u673a\u566a\u58f0<\/strong>\u7684\u654f\u611f\u7a0b\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u9ad8\u65b9\u5dee\u7684\u8868\u73b0<\/strong>\uff1a\u6a21\u578b\u8fc7\u4e8e\u590d\u6742\uff0c\u5bf9\u8bad\u7ec3\u6570\u636e\u4e2d\u7684\u5fae\u5c0f\u53d8\u5316\uff08\u5305\u62ec\u566a\u58f0\uff09\u53cd\u5e94\u8fc7\u5ea6\uff0c\u5bfc\u81f4<strong>\u8fc7\u62df\u5408<\/strong>\u3002\u6362\u4e00\u7ec4\u6570\u636e\u8bad\u7ec3\uff0c\u5f97\u5230\u7684\u6a21\u578b\u53ef\u80fd\u5b8c\u5168\u4e0d\u540c\u3002<\/li>\n\n\n\n<li><strong>\u4f8b\u5b50<\/strong>\uff1a\u4e00\u4e2a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u5982\u679c\u4e0d\u5bf9\u5176\u8fdb\u884c\u4efb\u4f55\u7ea6\u675f\uff0c\u5b83\u53ef\u80fd\u4f1a\u4e3a\u6bcf\u4e00\u5957\u72ec\u7279\u7684\u8bad\u7ec3\u6570\u636e\u751f\u6210\u4e00\u5957\u5b8c\u5168\u4e0d\u540c\u7684\u3001\u6781\u5ea6\u590d\u6742\u7684\u9884\u6d4b\u89c4\u5219\uff0c\u8fd9\u5c31\u662f\u9ad8\u65b9\u5dee\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd393. \u504f\u5dee-\u65b9\u5dee\u6743\u8861<\/strong><\/p>\n\n\n\n<p>\u8fd9\u662f\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u6838\u5fc3\u6743\u8861\u3002<strong>\u6211\u4eec\u65e0\u6cd5\u540c\u65f6\u6700\u5c0f\u5316\u504f\u5dee\u548c\u65b9\u5dee\u3002<\/strong><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"703\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-1024x703.png\" alt=\"\" class=\"wp-image-20117\" style=\"width:502px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-1024x703.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-300x206.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-768x527.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-830x570.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-230x158.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-350x240.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75-480x329.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-75.png 1167w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<ul>\n<li><strong>\u589e\u52a0\u6a21\u578b\u590d\u6742\u5ea6<\/strong>\uff1a\u901a\u5e38\u53ef\u4ee5<strong>\u964d\u4f4e\u504f\u5dee<\/strong>\uff08\u6a21\u578b\u80fd\u529b\u53d8\u5f3a\uff09\uff0c\u4f46\u4f1a<strong>\u589e\u52a0\u65b9\u5dee<\/strong>\uff08\u66f4\u5bb9\u6613\u5b66\u5230\u566a\u58f0\uff09\u3002<\/li>\n\n\n\n<li><strong>\u964d\u4f4e\u6a21\u578b\u590d\u6742\u5ea6<\/strong>\uff1a\u901a\u5e38\u53ef\u4ee5<strong>\u964d\u4f4e\u65b9\u5dee<\/strong>\uff08\u6a21\u578b\u66f4\u7a33\u5b9a\uff09\uff0c\u4f46\u4f1a<strong>\u589e\u52a0\u504f\u5dee<\/strong>\uff08\u6a21\u578b\u80fd\u529b\u53d8\u5f31\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u6211\u4eec\u7684\u76ee\u6807\u5c31\u662f\u627e\u5230\u56fe\u4e2d\u7684&#8221;\u6700\u4f73\u70b9&#8221;\uff0c\u4f7f\u5f97\u603b\u8bef\u5dee\u6700\u5c0f\u3002<\/p>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u8bca\u65ad\u4e0e\u5e94\u5bf9\u7b56\u7565\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u5982\u4f55\u5224\u65ad\u6a21\u578b\u5904\u4e8e\u54ea\u79cd\u72b6\u6001\uff1f\u5982\u4f55\u89e3\u51b3\uff1f<\/p>\n\n\n\n<p><strong>\ud83d\udd391. \u8bca\u65ad\u65b9\u6cd5\uff1a\u5b66\u4e60\u66f2\u7ebf<\/strong><\/p>\n\n\n\n<p>\u5b66\u4e60\u66f2\u7ebf\u662f\u7ed8\u5236\u6a21\u578b\u5728<strong>\u8bad\u7ec3\u96c6<\/strong>\u548c<strong>\u9a8c\u8bc1\u96c6<\/strong>\u4e0a\u7684\u6027\u80fd\uff08\u5982\u8bef\u5dee\uff09\u968f<strong>\u8bad\u7ec3\u6837\u672c\u6570<\/strong>\u6216<strong>\u6a21\u578b\u590d\u6742\u5ea6<\/strong>\u53d8\u5316\u7684\u66f2\u7ebf\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures, StandardScaler\nfrom sklearn.metrics import mean_squared_error\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 start --------------------------\nplt.rcParams['font.sans-serif'] = [\n    # Windows \u4f18\u5148\n    'SimHei', 'Microsoft YaHei',\n    # macOS \u4f18\u5148\n    'PingFang SC', 'Heiti TC',\n    # Linux \u4f18\u5148\n    'WenQuanYi Micro Hei', 'DejaVu Sans'\n]\n# \u4fee\u590d\u8d1f\u53f7\u663e\u793a\u4e3a\u65b9\u5757\u7684\u95ee\u9898\nplt.rcParams['axes.unicode_minus'] = False\n# \u8bbe\u7f6e\u56fe\u8868\u6837\u5f0f\nplt.rcParams['figure.figsize'] = (10, 6)\nplt.rcParams['axes.grid'] = True\nplt.rcParams['grid.alpha'] = 0.3\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 end --------------------------\n\n# \u52a0\u8f7d\u6570\u636e\ndata = load_diabetes()\nX, y = data.data, data.target\n# \u53ea\u4f7f\u7528\u4e00\u4e2a\u7279\u5f81\uff08\u66f4\u9002\u5408\u591a\u9879\u5f0f\u56de\u5f52\u6f14\u793a\uff09\nX = X[:, np.newaxis, 2]  # \u9009\u62e9\u7b2c\u4e09\u4e2a\u7279\u5f81\uff08BMI\uff09\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \u5b9a\u4e49\u5b66\u4e60\u66f2\u7ebf\u7ed8\u5236\u51fd\u6570\uff08\u4f18\u5316\u7248\uff09\ndef plot_learning_curve(estimator, title, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 10)):\n    \"\"\"\n    \u7ed8\u5236\u5b66\u4e60\u66f2\u7ebf\n    \u53c2\u6570\uff1a\n        estimator: \u6a21\u578b\u4f30\u8ba1\u5668\n        title: \u56fe\u8868\u6807\u9898\n        X: \u7279\u5f81\u6570\u636e\n        y: \u76ee\u6807\u53d8\u91cf\n        cv: \u4ea4\u53c9\u9a8c\u8bc1\u6298\u6570\n        train_sizes: \u8bad\u7ec3\u6837\u672c\u6bd4\u4f8b\n    \"\"\"\n    # \u83b7\u53d6\u5b66\u4e60\u66f2\u7ebf\u6570\u636e\n    train_sizes_abs, train_scores, test_scores = learning_curve(\n        estimator, X, y, cv=cv, scoring='neg_mean_squared_error',\n        train_sizes=train_sizes, random_state=42, n_jobs=-1\n    )\n    \n    # \u8ba1\u7b97\u5747\u503c\u548c\u6807\u51c6\u5dee\n    train_scores_mean = -train_scores.mean(axis=1)\n    train_scores_std = train_scores.std(axis=1)\n    test_scores_mean = -test_scores.mean(axis=1)\n    test_scores_std = test_scores.std(axis=1)\n    \n    # \u7ed8\u5236\u5b66\u4e60\u66f2\u7ebf\n    plt.figure(figsize=(10, 6))\n    plt.fill_between(train_sizes_abs, \n                     train_scores_mean - train_scores_std,\n                     train_scores_mean + train_scores_std, \n                     alpha=0.1, color='r')\n    plt.fill_between(train_sizes_abs,\n                     test_scores_mean - test_scores_std,\n                     test_scores_mean + test_scores_std,\n                     alpha=0.1, color='g')\n    \n    # \u7ed8\u5236\u5747\u503c\u66f2\u7ebf\n    plt.plot(train_sizes_abs, train_scores_mean, 'o-', color='r', linewidth=2,\n             markersize=8, label='\u8bad\u7ec3\u96c6 MSE')\n    plt.plot(train_sizes_abs, test_scores_mean, 'o-', color='g', linewidth=2,\n             markersize=8, label='\u9a8c\u8bc1\u96c6 MSE')\n    \n    # \u8bbe\u7f6e\u56fe\u8868\u5c5e\u6027\n    plt.xlabel('\u8bad\u7ec3\u6837\u672c\u6570\u91cf', fontsize=12)\n    plt.ylabel('\u5747\u65b9\u8bef\u5dee (MSE)', fontsize=12)\n    plt.title(title, fontsize=14, pad=20)\n    plt.legend(loc='upper right', fontsize=11)\n    plt.tight_layout()\n    plt.show()\n    \n    # \u6253\u5370\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0\n    estimator.fit(X_train, y_train)\n    y_pred = estimator.predict(X_test)\n    mse = mean_squared_error(y_test, y_pred)\n    print(f\"{title} - \u6d4b\u8bd5\u96c6 MSE: {mse:.2f}\")\n\n# 1. \u6b20\u62df\u5408\u6a21\u578b\uff081\u9636\u591a\u9879\u5f0f - \u7ebf\u6027\u56de\u5f52\uff09\nprint(\"=\"*60)\nprint(\"\u6b20\u62df\u5408\u6a21\u578b\uff081\u9636\u591a\u9879\u5f0f - \u7ebf\u6027\u56de\u5f52\uff09\")\nprint(\"=\"*60)\nplot_learning_curve(\n    make_pipeline(StandardScaler(), PolynomialFeatures(1), LinearRegression()),\n    '\u6b20\u62df\u5408\u6a21\u578b\u5b66\u4e60\u66f2\u7ebf\uff081\u9636\u591a\u9879\u5f0f\uff09',\n    X, y\n)\n\n# 2. \u826f\u597d\u62df\u5408\u6a21\u578b\uff082\u9636\u591a\u9879\u5f0f\uff09\nprint(\"\\n\" + \"=\"*60)\nprint(\"\u826f\u597d\u62df\u5408\u6a21\u578b\uff082\u9636\u591a\u9879\u5f0f\uff09\")\nprint(\"=\"*60)\nplot_learning_curve(\n    make_pipeline(StandardScaler(), PolynomialFeatures(2), LinearRegression()),\n    '\u826f\u597d\u62df\u5408\u6a21\u578b\u5b66\u4e60\u66f2\u7ebf\uff082\u9636\u591a\u9879\u5f0f\uff09',\n    X, y\n)\n\n# 3. \u8fc7\u62df\u5408\u6a21\u578b\uff088\u9636\u591a\u9879\u5f0f\uff09\nprint(\"\\n\" + \"=\"*60)\nprint(\"\u8fc7\u62df\u5408\u6a21\u578b\uff088\u9636\u591a\u9879\u5f0f\uff09\")\nprint(\"=\"*60)\nplot_learning_curve(\n    make_pipeline(StandardScaler(), PolynomialFeatures(8), LinearRegression()),\n    '\u8fc7\u62df\u5408\u6a21\u578b\u5b66\u4e60\u66f2\u7ebf\uff088\u9636\u591a\u9879\u5f0f\uff09',\n    X, y\n)\n\n# \u989d\u5916\uff1a\u53ef\u89c6\u5316\u4e0d\u540c\u9636\u6570\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\nplt.figure(figsize=(12, 8))\nX_plot = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)\n\n# \u7ed8\u5236\u539f\u59cb\u6570\u636e\u70b9\nplt.scatter(X_train, y_train, alpha=0.5, label='\u8bad\u7ec3\u6570\u636e', color='blue', s=30)\nplt.scatter(X_test, y_test, alpha=0.5, label='\u6d4b\u8bd5\u6570\u636e', color='orange', s=30)\n\n# \u7ed8\u5236\u4e0d\u540c\u9636\u6570\u7684\u62df\u5408\u66f2\u7ebf\norders = [1, 2, 8]\ncolors = ['red', 'green', 'purple']\nlabels = ['1\u9636\uff08\u6b20\u62df\u5408\uff09', '2\u9636\uff08\u826f\u597d\u62df\u5408\uff09', '8\u9636\uff08\u8fc7\u62df\u5408\uff09']\n\nfor i, order in enumerate(orders):\n    model = make_pipeline(StandardScaler(), PolynomialFeatures(order), LinearRegression())\n    model.fit(X_train, y_train)\n    y_plot = model.predict(X_plot)\n    plt.plot(X_plot, y_plot, color=colors[i], linewidth=2, label=labels[i])\n\nplt.xlabel('BMI \u7279\u5f81\uff08\u6807\u51c6\u5316\uff09', fontsize=12)\nplt.ylabel('\u7cd6\u5c3f\u75c5\u8fdb\u5c55\u6307\u6807', fontsize=12)\nplt.title('\u4e0d\u540c\u9636\u6570\u591a\u9879\u5f0f\u56de\u5f52\u7684\u62df\u5408\u6548\u679c\u5bf9\u6bd4', fontsize=14, pad=20)\nplt.legend(fontsize=11)\nplt.tight_layout()\nplt.show()<\/pre>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"625\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-1024x625.png\" alt=\"\" class=\"wp-image-20119\" style=\"width:616px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-1024x625.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-300x183.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-768x469.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-1536x938.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-830x507.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-230x140.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-350x214.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77-480x293.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-77.png 1647w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"636\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-1024x636.png\" alt=\"\" class=\"wp-image-20121\" style=\"width:590px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-1024x636.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-300x186.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-768x477.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-1536x954.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-830x515.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-230x143.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-350x217.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79-480x298.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-79.png 1649w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"619\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-1024x619.png\" alt=\"\" class=\"wp-image-20122\" style=\"width:584px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-1024x619.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-300x181.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-768x464.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-1536x928.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-830x502.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-230x139.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-350x211.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80-480x290.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-80.png 1655w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"729\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-1024x729.png\" alt=\"\" class=\"wp-image-20124\" style=\"width:594px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-1024x729.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-300x214.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-768x547.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-1536x1093.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-830x591.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-230x164.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-350x249.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82-480x342.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-82.png 1665w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p><strong>\u5982\u4f55\u89e3\u8bfb\u5b66\u4e60\u66f2\u7ebf\uff1f<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table><thead><tr><th>\u62df\u5408\u72b6\u6001<\/th><th>\u8bad\u7ec3\u8bef\u5dee<\/th><th>\u9a8c\u8bc1\u8bef\u5dee<\/th><th>\u66f2\u7ebf\u7279\u5f81<\/th><\/tr><\/thead><tbody><tr><td><strong>\u6b20\u62df\u5408<\/strong><\/td><td><strong>\u9ad8<\/strong><\/td><td><strong>\u9ad8<\/strong><\/td><td>\u4e24\u6761\u66f2\u7ebf\u90fd\u5f88\u9ad8\u4e14\u975e\u5e38\u63a5\u8fd1\uff0c\u589e\u52a0\u6570\u636e\u65e0\u5e2e\u52a9\u3002<\/td><\/tr><tr><td><strong>\u826f\u597d\u62df\u5408<\/strong><\/td><td><strong>\u4f4e<\/strong><\/td><td><strong>\u4f4e<\/strong><\/td><td>\u4e24\u6761\u66f2\u7ebf\u90fd\u8f83\u4f4e\u4e14\u5f7c\u6b64\u63a5\u8fd1\uff0c\u8fbe\u5230\u4e00\u4e2a\u5e73\u8861\u70b9\u3002<\/td><\/tr><tr><td><strong>\u8fc7\u62df\u5408<\/strong><\/td><td><strong>\u975e\u5e38\u4f4e<\/strong><\/td><td><strong>\u9ad8<\/strong><\/td><td>\u8bad\u7ec3\u8bef\u5dee\u5f88\u4f4e\uff0c\u4f46\u9a8c\u8bc1\u8bef\u5dee\u5f88\u9ad8\uff0c\u4e2d\u95f4\u6709\u660e\u663e\u95f4\u9699\u3002\u589e\u52a0\u6570\u636e\u901a\u5e38\u80fd\u4f7f\u4e24\u8005\u9760\u8fd1\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd392. \u5e94\u5bf9\u7b56\u7565<\/strong><\/p>\n\n\n\n<p>\u6839\u636e\u8bca\u65ad\u7ed3\u679c\uff0c\u6211\u4eec\u53ef\u4ee5\u91c7\u53d6\u4e0d\u540c\u7b56\u7565\uff1a<\/p>\n\n\n\n<p>\u89e3\u51b3\u6b20\u62df\u5408\uff08\u9ad8\u504f\u5dee\uff09\uff1a<\/p>\n\n\n\n<ul>\n<li>\u589e\u52a0\u6a21\u578b\u590d\u6742\u5ea6\uff1a\u4f7f\u7528\u66f4\u5f3a\u5927\u7684\u6a21\u578b\uff08\u5982\u4ece\u7ebf\u6027\u6a21\u578b\u5207\u6362\u5230\u6811\u6a21\u578b\u3001\u795e\u7ecf\u7f51\u7edc\uff09\u3002<\/li>\n\n\n\n<li>\u6dfb\u52a0\u66f4\u591a\u7279\u5f81\uff1a\u6316\u6398\u6216\u6784\u9020\u66f4\u6709\u610f\u4e49\u7684\u7279\u5f81\u3002<\/li>\n\n\n\n<li>\u51cf\u5c11\u6b63\u5219\u5316\uff1a\u5982\u679c\u4f7f\u7528\u4e86\u6b63\u5219\u5316\uff08\u5982 L1\u3001L2\uff09\uff0c\u5c1d\u8bd5\u51cf\u5f31\u5176\u5f3a\u5ea6\u3002<\/li>\n\n\n\n<li>\u5ef6\u957f\u8bad\u7ec3\u65f6\u95f4\uff1a\u5bf9\u4e8e\u8fed\u4ee3\u6a21\u578b\uff08\u5982\u795e\u7ecf\u7f51\u7edc\uff09\uff0c\u8bad\u7ec3\u66f4\u591a\u8f6e\u6b21\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u89e3\u51b3\u8fc7\u62df\u5408\uff08\u9ad8\u65b9\u5dee\uff09\uff1a<\/p>\n\n\n\n<ul>\n<li>\u83b7\u53d6\u66f4\u591a\u8bad\u7ec3\u6570\u636e\uff1a\u6700\u6709\u6548\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002<\/li>\n\n\n\n<li>\u964d\u4f4e\u6a21\u578b\u590d\u6742\u5ea6\uff1a\u9009\u62e9\u66f4\u7b80\u5355\u7684\u6a21\u578b\uff08\u5982\u964d\u4f4e\u591a\u9879\u5f0f\u9636\u6570\u3001\u51cf\u5c11\u6811\u6df1\u5ea6\u3001\u51cf\u5c11\u795e\u7ecf\u7f51\u7edc\u5c42\u6570\uff09\u3002<\/li>\n\n\n\n<li>\u7279\u5f81\u9009\u62e9\uff1a\u79fb\u9664\u4e0d\u76f8\u5173\u6216\u5197\u4f59\u7684\u7279\u5f81\u3002<\/li>\n\n\n\n<li>\u589e\u52a0\u6b63\u5219\u5316\uff1a\n<ul>\n<li>L1 \u6b63\u5219\u5316 (Lasso)\uff1a\u503e\u5411\u4e8e\u4ea7\u751f\u7a00\u758f\u6743\u91cd\uff0c\u53ef\u7528\u4e8e\u7279\u5f81\u9009\u62e9\u3002<\/li>\n\n\n\n<li>L2 \u6b63\u5219\u5316 (Ridge)\uff1a\u4f7f\u6743\u91cd\u8870\u51cf\uff0c\u503e\u5411\u4e8e\u8ba9\u6240\u6709\u6743\u91cd\u90fd\u8f83\u5c0f\u3002<\/li>\n\n\n\n<li>Dropout\uff08\u7528\u4e8e\u795e\u7ecf\u7f51\u7edc\uff09\uff1a\u5728\u8bad\u7ec3\u4e2d\u968f\u673a&#8221;\u4e22\u5f03&#8221;\u4e00\u90e8\u5206\u795e\u7ecf\u5143\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>\u65e9\u505c\uff08\u7528\u4e8e\u8fed\u4ee3\u6a21\u578b\uff09\uff1a\u5f53\u9a8c\u8bc1\u96c6\u8bef\u5dee\u4e0d\u518d\u4e0b\u964d\u65f6\u505c\u6b62\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u5b9e\u8df5\u7ec3\u4e60\uff1a\u5728\u771f\u5b9e\u6570\u636e\u96c6\u4e0a\u4f53\u9a8c\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u8ba9\u6211\u4eec\u5728\u7ecf\u5178\u7684\u6ce2\u58eb\u987f\u623f\u4ef7\u6570\u636e\u96c6\uff08\u6216\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6\uff0c\u56e0\u4e3a\u6ce2\u58eb\u987f\u6570\u636e\u96c6\u5df2\u5f03\u7528\uff09\u4e0a\u5b9e\u8df5\u4e00\u4e0b\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.metrics import mean_squared_error\n\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 start --------------------------\nplt.rcParams['font.sans-serif'] = [\n    # Windows \u4f18\u5148\n    'SimHei', 'Microsoft YaHei',\n    # macOS \u4f18\u5148\n    'PingFang SC', 'Heiti TC',\n    # Linux \u4f18\u5148\n    'WenQuanYi Micro Hei', 'DejaVu Sans'\n]\n# \u4fee\u590d\u8d1f\u53f7\u663e\u793a\u4e3a\u65b9\u5757\u7684\u95ee\u9898\nplt.rcParams['axes.unicode_minus'] = False\n# -------------------------- \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53 end --------------------------\n\n# \u52a0\u8f7d\u6570\u636e\ndata = load_diabetes()\nX, y = data.data, data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \u5c1d\u8bd5\u4e0d\u540c\u590d\u6742\u5ea6\u7684\u51b3\u7b56\u6811\nmax_depths = [1, 3, 10, None]  # None \u8868\u793a\u4e0d\u9650\u5236\u6df1\u5ea6\uff0c\u6811\u4f1a\u4e00\u76f4\u751f\u957f\u76f4\u5230\"\u7eaf\"\ntrain_errors = []\ntest_errors = []\n\nfor depth in max_depths:\n    model = DecisionTreeRegressor(max_depth=depth, random_state=42)\n    model.fit(X_train, y_train)\n\n    y_train_pred = model.predict(X_train)\n    y_test_pred = model.predict(X_test)\n\n    train_error = mean_squared_error(y_train, y_train_pred)\n    test_error = mean_squared_error(y_test, y_test_pred)\n\n    train_errors.append(train_error)\n    test_errors.append(test_error)\n\n    print(f\"\u6811\u6700\u5927\u6df1\u5ea6: {depth if depth is not None else '\u65e0\u9650\u5236'}\")\n    print(f\"  \u8bad\u7ec3\u96c6 MSE: {train_error:.2f}\")\n    print(f\"  \u6d4b\u8bd5\u96c6 MSE: {test_error:.2f}\")\n    print(\"-\" * 30)\n\n# \u53ef\u89c6\u5316\nplt.figure(figsize=(10, 6))\ndepths = [str(d) if d else '\u65e0\u9650\u5236' for d in max_depths]\nx_index = np.arange(len(depths))\nwidth = 0.35\n\nplt.bar(x_index - width\/2, train_errors, width, label='\u8bad\u7ec3\u8bef\u5dee', color='skyblue')\nplt.bar(x_index + width\/2, test_errors, width, label='\u6d4b\u8bd5\u8bef\u5dee', color='lightcoral')\n\nplt.xlabel('\u51b3\u7b56\u6811\u6700\u5927\u6df1\u5ea6 (\u6a21\u578b\u590d\u6742\u5ea6)')\nplt.ylabel('\u5747\u65b9\u8bef\u5dee (MSE)')\nplt.title('\u504f\u5dee-\u65b9\u5dee\u6743\u8861\uff1a\u4e0d\u540c\u590d\u6742\u5ea6\u51b3\u7b56\u6811\u7684\u8868\u73b0')\nplt.xticks(x_index, depths)\nplt.legend()\nplt.grid(True, axis='y')\nplt.tight_layout()\nplt.show()<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\u6811\u6700\u5927\u6df1\u5ea6: 1\n  \u8bad\u7ec3\u96c6 MSE: 4227.29\n  \u6d4b\u8bd5\u96c6 MSE: 4606.59\n------------------------------\n\u6811\u6700\u5927\u6df1\u5ea6: 3\n  \u8bad\u7ec3\u96c6 MSE: 2935.04\n  \u6d4b\u8bd5\u96c6 MSE: 3552.70\n------------------------------\n\u6811\u6700\u5927\u6df1\u5ea6: 10\n  \u8bad\u7ec3\u96c6 MSE: 375.60\n  \u6d4b\u8bd5\u96c6 MSE: 4387.98\n------------------------------\n\u6811\u6700\u5927\u6df1\u5ea6: \u65e0\u9650\u5236\n  \u8bad\u7ec3\u96c6 MSE: 0.00\n  \u6d4b\u8bd5\u96c6 MSE: 4976.80\n------------------------------<\/pre>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"609\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-1024x609.png\" alt=\"\" class=\"wp-image-20125\" style=\"width:550px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-1024x609.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-300x178.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-768x457.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-1536x913.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-830x493.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-230x137.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-350x208.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83-480x285.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-83.png 1662w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p><strong>\u5206\u6790\u7ed3\u679c<\/strong>\uff1a<\/p>\n\n\n\n<ul>\n<li><strong>\u6df1\u5ea6=1<\/strong>\uff1a\u6a21\u578b\u975e\u5e38\u7b80\u5355\uff0c\u8bad\u7ec3\u548c\u6d4b\u8bd5\u8bef\u5dee\u90fd\u8f83\u9ad8 ->\u00a0<strong>\u9ad8\u504f\u5dee\uff0c\u6b20\u62df\u5408<\/strong>\u3002<\/li>\n\n\n\n<li><strong>\u6df1\u5ea6=3<\/strong>\uff1a\u6a21\u578b\u590d\u6742\u5ea6\u589e\u52a0\uff0c\u4e24\u9879\u8bef\u5dee\u90fd\u663e\u8457\u4e0b\u964d\uff0c\u4e14\u6bd4\u8f83\u63a5\u8fd1 ->\u00a0<strong>\u504f\u5dee\u4e0e\u65b9\u5dee\u5e73\u8861\uff0c\u826f\u597d\u62df\u5408<\/strong>\u3002<\/li>\n\n\n\n<li><strong>\u6df1\u5ea6=10 \u6216 \u65e0\u9650\u5236<\/strong>\uff1a\u6a21\u578b\u975e\u5e38\u590d\u6742\uff0c\u8bad\u7ec3\u8bef\u5dee\u6781\u4f4e\uff0c\u4f46\u6d4b\u8bd5\u8bef\u5dee\u5f00\u59cb\u4e0a\u5347\uff08\u6216\u8fdc\u9ad8\u4e8e\u8bad\u7ec3\u8bef\u5dee\uff09 ->\u00a0<strong>\u9ad8\u65b9\u5dee\uff0c\u8fc7\u62df\u5408<\/strong>\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u603b\u7ed3\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u7406\u89e3\u8fc7\u62df\u5408\u3001\u6b20\u62df\u5408\u3001\u504f\u5dee\u4e0e\u65b9\u5dee\uff0c\u662f\u6784\u5efa\u4f18\u79c0\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u57fa\u77f3\u3002\u8bb0\u4f4f\u8fd9\u4e2a\u6838\u5fc3\u5faa\u73af\uff1a<\/p>\n\n\n\n<ol>\n<li>\u8bad\u7ec3\u6a21\u578b\u00a0->\u00a0\u8bc4\u4f30\u5176\u5728\u8bad\u7ec3\u96c6\u548c\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0\u3002<\/li>\n\n\n\n<li>\u901a\u8fc7\u5b66\u4e60\u66f2\u7ebf\u6216\u8bef\u5dee\u5bf9\u6bd4\u8bca\u65ad\u95ee\u9898\uff1a\u662f\u9ad8\u504f\u5dee\uff08\u6b20\u62df\u5408\uff09\u8fd8\u662f\u9ad8\u65b9\u5dee\uff08\u8fc7\u62df\u5408\uff09\uff1f<\/li>\n\n\n\n<li>\u5e94\u7528\u76f8\u5e94\u7684\u7b56\u7565\uff08\u589e\u52a0\u590d\u6742\u5ea6\/\u6570\u636e\u3001\u6b63\u5219\u5316\u7b49\uff09\u8fdb\u884c\u6539\u8fdb\u3002<\/li>\n\n\n\n<li>\u56de\u5230\u7b2c 1 \u6b65\uff0c\u76f4\u5230\u5728\u9a8c\u8bc1\u96c6\u4e0a\u83b7\u5f97\u6ee1\u610f\u7684\u3001\u6cdb\u5316\u80fd\u529b\u5f3a\u7684\u6a21\u578b\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>\u524d\u8a00\u200b\ud83d\udd16 \u5728\u673a\u5668\u5b66\u4e60\u7684\u4e16\u754c\u91cc\uff0c\u6784\u5efa\u4e00\u4e2a\u6a21\u578b\u5c31\u50cf\u8bad\u7ec3\u4e00\u4f4d\u5b66\u751f\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u5e0c\u671b\u8fd9\u4f4d\u5b66\u751f\u4e0d\u4ec5\u80fd\u8bb0\u4f4f\u8bfe\u672c\u4e0a\u7684\u4f8b\u9898\uff08\u8bad [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"_links":{"self":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20114"}],"collection":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20114"}],"version-history":[{"count":2,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20114\/revisions"}],"predecessor-version":[{"id":20127,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20114\/revisions\/20127"}],"wp:attachment":[{"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20114"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20114"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}