{"id":945846,"date":"2024-12-26T23:29:47","date_gmt":"2024-12-26T15:29:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/945846.html"},"modified":"2024-12-26T23:29:49","modified_gmt":"2024-12-26T15:29:49","slug":"python%e5%a6%82%e4%bd%95%e6%8b%9f%e5%90%88%e6%95%a3%e7%82%b9%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/945846.html","title":{"rendered":"python\u5982\u4f55\u62df\u5408\u6563\u70b9\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u62df\u5408\u6563\u70b9\u56fe\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:
Python\u62df\u5408\u6563\u70b9\u56fe\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u3001\u591a\u9879\u5f0f\u56de\u5f52\u3001\u975e\u7ebf\u6027\u56de\u5f52\u4ee5\u53ca\u66f2\u7ebf\u62df\u5408\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u7ebf\u6027\u56de\u5f52\u662f\u6700\u7b80\u5355\u3001\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u591a\u9879\u5f0f\u56de\u5f52\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u975e\u7ebf\u6027\u5173\u7cfb\u7684\u6570\u636e\uff0c\u800c\u975e\u7ebf\u6027\u56de\u5f52\u548c\u66f2\u7ebf\u62df\u5408\u5219\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u96c6\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u5e94\u7528\u573a\u666f\u3002\u7279\u522b\u662f\u7ebf\u6027\u56de\u5f52\uff0c\u901a\u8fc7\u4f7f\u7528Python\u7684scikit-learn<\/code>\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u6563\u70b9\u56fe\u8fdb\u884c\u7ebf\u6027\u62df\u5408\u3002\u6211\u4eec\u5c06\u4e00\u6b65\u6b65\u6f14\u793a\u5982\u4f55\u4f7f\u7528\u8fd9\u7c7b\u5e93\u6765\u5b9e\u73b0\u6563\u70b9\u56fe\u7684\u62df\u5408\uff0c\u4ee5\u5e2e\u52a9\u7406\u89e3\u6570\u636e\u7684\u6f5c\u5728\u6a21\u5f0f\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u7ebf\u6027\u56de\u5f52\u62df\u5408<\/p>\n<\/p>\n

\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u57fa\u672c\u7684\u7edf\u8ba1\u65b9\u6cd5\uff0c\u7528\u4e8e\u5efa\u7acb\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u5b83\u5047\u8bbe\u56e0\u53d8\u91cf\u548c\u81ea\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u7ebf\u6027\u5173\u7cfb\uff0c\u9002\u7528\u4e8e\u6570\u636e\u70b9\u5448\u7ebf\u6027\u5206\u5e03\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n

1.1 \u4f7f\u7528scikit-learn<\/code>\u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/p>\n<\/p>\n

scikit-learn<\/code>\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60<\/a>\u5e93\uff0c\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u6240\u9700\u7684\u5e93\u5e76\u51c6\u5907\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u751f\u6210\u6a21\u62df\u6570\u636e<\/strong><\/h2>\n
np.random.seed(0)<\/p>\n
X = 2 * np.random.rand(100, 1)<\/p>\n
y = 4 + 3 * X + np.random.randn(100, 1)<\/p>\n
\u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u5e76\u62df\u5408\u6570\u636e<\/strong><\/h2>\n
lin_reg = LinearRegression()<\/p>\n
lin_reg.fit(X, y)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u56de\u5f52\u76f4\u7ebf<\/strong><\/h2>\n
plt.scatter(X, y, color='blue', label='Data Points')<\/p>\n
plt.plot(X, lin_reg.predict(X), color='red', label='Fitted Line')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Linear Regression Fit')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u7ec4\u6a21\u62df\u6570\u636e\uff0c\u5e76\u4f7f\u7528LinearRegression<\/code>\u7c7b\u5bf9\u6570\u636e\u8fdb\u884c\u62df\u5408\u3002\u901a\u8fc7fit<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8ba1\u7b97\u51fa\u6700\u4f73\u62df\u5408\u7684\u76f4\u7ebf\u53c2\u6570\uff0c\u4ece\u800c\u7ed8\u5236\u51fa\u56de\u5f52\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
1.2 \u7ebf\u6027\u56de\u5f52\u7684\u4f18\u7f3a\u70b9<\/p>\n<\/p>\n
\u7ebf\u6027\u56de\u5f52\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u7b80\u5355\u6027\u548c\u6613\u89e3\u91ca\u6027\u3002\u7531\u4e8e\u53c2\u6570\u5c11\u4e14\u8ba1\u7b97\u7b80\u5355\uff0c\u7ebf\u6027\u56de\u5f52\u5728\u5904\u7406\u7ebf\u6027\u5173\u7cfb\u7684\u6570\u636e\u65f6\u8868\u73b0\u51fa\u8272\u3002\u7136\u800c\uff0c\u5f53\u6570\u636e\u4e2d\u5b58\u5728\u975e\u7ebf\u6027\u5173\u7cfb\u65f6\uff0c\u7ebf\u6027\u56de\u5f52\u7684\u8868\u73b0\u53ef\u80fd\u4e0d\u4f73\u3002\u56e0\u6b64\uff0c\u5728\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u524d\uff0c\u9700\u8bc4\u4f30\u6570\u636e\u7279\u5f81\u4ee5\u786e\u4fdd\u5176\u9002\u7528\u6027\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u591a\u9879\u5f0f\u56de\u5f52\u62df\u5408<\/p>\n<\/p>\n
\u591a\u9879\u5f0f\u56de\u5f52\u662f\u5bf9\u7ebf\u6027\u56de\u5f52\u7684\u4e00\u79cd\u63a8\u5e7f\uff0c\u5b83\u901a\u8fc7\u5f15\u5165\u591a\u9879\u5f0f\u7279\u5f81\u6765\u6355\u6349\u6570\u636e\u4e2d\u7684\u975e\u7ebf\u6027\u5173\u7cfb\u3002\u591a\u9879\u5f0f\u56de\u5f52\u80fd\u591f\u5904\u7406\u590d\u6742\u7684\u975e\u7ebf\u6027\u6570\u636e\u5206\u5e03\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528numpy<\/code>\u548cmatplotlib<\/code>\u8fdb\u884c\u591a\u9879\u5f0f\u56de\u5f52<\/p>\n<\/p>\n
\u5728\u8fdb\u884c\u591a\u9879\u5f0f\u56de\u5f52\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528numpy<\/code>\u5e93\u4e2d\u7684polyfit<\/code>\u51fd\u6570\u6765\u62df\u5408\u6570\u636e\uff0c\u5e76\u4f7f\u7528matplotlib<\/code>\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u6a21\u62df\u6570\u636e<\/p>\n
np.random.seed(1)<\/p>\n
X = np.linspace(-3, 3, 100)<\/p>\n
y = X2 + 2*X + np.random.randn(100)<\/p>\n
\u4f7f\u7528numpy\u8fdb\u884c\u591a\u9879\u5f0f\u62df\u5408<\/strong><\/h2>\n
coefficients = np.polyfit(X, y, deg=2)<\/p>\n
polynomial = np.poly1d(coefficients)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u591a\u9879\u5f0f\u62df\u5408\u66f2\u7ebf<\/strong><\/h2>\n
plt.scatter(X, y, color='blue', label='Data Points')<\/p>\n
plt.plot(X, polynomial(X), color='red', label='Polynomial Fit')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Polynomial Regression Fit')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u62df\u5408\u4e86\u4e00\u4e2a\u4e8c\u6b21\u591a\u9879\u5f0f\u3002\u901a\u8fc7\u6307\u5b9adeg<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u8c03\u6574\u591a\u9879\u5f0f\u7684\u9636\u6570\u4ee5\u9002\u5e94\u6570\u636e\u7684\u590d\u6742\u6027\u3002<\/p>\n<\/p>\n
2.2 \u591a\u9879\u5f0f\u56de\u5f52\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
\u591a\u9879\u5f0f\u56de\u5f52\u9002\u7528\u4e8e\u6570\u636e\u70b9\u5448\u73b0\u975e\u7ebf\u6027\u5173\u7cfb\u7684\u60c5\u51b5\uff0c\u4f8b\u5982\u629b\u7269\u7ebf\u6216\u6ce2\u52a8\u6a21\u5f0f\u3002\u7136\u800c\uff0c\u9009\u62e9\u591a\u9879\u5f0f\u7684\u9636\u6570\u65f6\u9700\u8c28\u614e\uff0c\u4ee5\u907f\u514d\u8fc7\u62df\u5408\u3002\u8fc7\u9ad8\u7684\u9636\u6570\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u8fc7\u4e8e\u590d\u6742\uff0c\u65e0\u6cd5\u5f88\u597d\u5730\u63a8\u5e7f\u5230\u65b0\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u975e\u7ebf\u6027\u56de\u5f52\u62df\u5408<\/p>\n<\/p>\n
\u975e\u7ebf\u6027\u56de\u5f52\u7528\u4e8e\u6570\u636e\u95f4\u5173\u7cfb\u4e0d\u7b26\u5408\u7ebf\u6027\u6216\u7b80\u5355\u591a\u9879\u5f0f\u5f62\u5f0f\u7684\u60c5\u51b5\u3002\u5b83\u901a\u8fc7\u4f18\u5316\u975e\u7ebf\u6027\u51fd\u6570\u7684\u53c2\u6570\u6765\u62df\u5408\u6570\u636e\u3002<\/p>\n<\/p>\n
3.1 \u4f7f\u7528scipy<\/code>\u8fdb\u884c\u975e\u7ebf\u6027\u56de\u5f52<\/p>\n<\/p>\n
scipy<\/code>\u5e93\u4e2d\u7684curve_fit<\/code>\u51fd\u6570\u63d0\u4f9b\u4e86\u975e\u7ebf\u6027\u56de\u5f52\u7684\u529f\u80fd\u3002\u901a\u8fc7\u5b9a\u4e49\u4e00\u4e2a\u975e\u7ebf\u6027\u6a21\u578b\u51fd\u6570\uff0c\u53ef\u4ee5\u62df\u5408\u590d\u6742\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
from scipy.optimize import curve_fit<\/p>\n
\u5b9a\u4e49\u975e\u7ebf\u6027\u6a21\u578b\u51fd\u6570<\/strong><\/h2>\n
def model_func(x, a, b, c):<\/p>\n
    return a * np.exp(-b * x) + c<\/p>\n
\u751f\u6210\u6a21\u62df\u6570\u636e<\/strong><\/h2>\n
np.random.seed(2)<\/p>\n
X = np.linspace(0, 4, 100)<\/p>\n
y = model_func(X, 2.5, 1.3, 0.5) + 0.2 * np.random.randn(100)<\/p>\n
\u4f7f\u7528curve_fit\u8fdb\u884c\u975e\u7ebf\u6027\u62df\u5408<\/strong><\/h2>\n
params, _ = curve_fit(model_func, X, y)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u975e\u7ebf\u6027\u62df\u5408\u66f2\u7ebf<\/strong><\/h2>\n
plt.scatter(X, y, color='blue', label='Data Points')<\/p>\n
plt.plot(X, model_func(X, *params), color='red', label='Nonlinear Fit')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Nonlinear Regression Fit')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e00\u4e2a\u6307\u6570\u8870\u51cf\u51fd\u6570\u4f5c\u4e3a\u975e\u7ebf\u6027\u6a21\u578b\uff0c\u5e76\u901a\u8fc7curve_fit<\/code>\u51fd\u6570\u6765\u62df\u5408\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
3.2 \u975e\u7ebf\u6027\u56de\u5f52\u7684\u4f18\u7f3a\u70b9<\/p>\n<\/p>\n
\u975e\u7ebf\u6027\u56de\u5f52\u5177\u6709\u9ad8\u5ea6\u7684\u7075\u6d3b\u6027\uff0c\u80fd\u591f\u62df\u5408\u591a\u79cd\u590d\u6742\u7684\u6a21\u5f0f\u3002\u7136\u800c\uff0c\u7531\u4e8e\u5176\u590d\u6742\u6027\uff0c\u975e\u7ebf\u6027\u56de\u5f52\u7684\u8ba1\u7b97\u6210\u672c\u8f83\u9ad8\uff0c\u4e14\u5bb9\u6613\u9677\u5165\u5c40\u90e8\u6700\u5c0f\u503c\u3002\u9009\u62e9\u5408\u9002\u7684\u521d\u59cb\u53c2\u6570\u548c\u6a21\u578b\u51fd\u6570\u662f\u6210\u529f\u8fdb\u884c\u975e\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u66f2\u7ebf\u62df\u5408\u65b9\u6cd5<\/p>\n<\/p>\n
\u66f2\u7ebf\u62df\u5408\u662f\u4e00\u79cd\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u6570\u5b66\u6280\u672f\uff0c\u65e8\u5728\u627e\u5230\u6700\u80fd\u63cf\u8ff0\u6570\u636e\u8d8b\u52bf\u7684\u66f2\u7ebf\u3002\u5b83\u4e0d\u4ec5\u9650\u4e8e\u7ebf\u6027\u6216\u591a\u9879\u5f0f\u51fd\u6570\uff0c\u53ef\u4ee5\u662f\u4efb\u4f55\u7b26\u5408\u6570\u636e\u5f62\u6001\u7684\u51fd\u6570\u3002<\/p>\n<\/p>\n
4.1 \u4f7f\u7528numpy<\/code>\u8fdb\u884c\u66f2\u7ebf\u62df\u5408<\/p>\n<\/p>\n
\u901a\u8fc7numpy<\/code>\u7684polyfit<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u7b80\u5355\u7684\u66f2\u7ebf\u62df\u5408\u3002\u5bf9\u4e8e\u590d\u6742\u7684\u66f2\u7ebf\uff0c\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u6570\u5b66\u5de5\u5177\u8fdb\u884c\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u6a21\u62df\u6570\u636e<\/p>\n
np.random.seed(3)<\/p>\n
X = np.linspace(-5, 5, 100)<\/p>\n
y = np.sin(X) + 0.2 * np.random.randn(100)<\/p>\n
\u4f7f\u7528numpy\u8fdb\u884c\u66f2\u7ebf\u62df\u5408<\/strong><\/h2>\n
coefficients = np.polyfit(X, y, deg=5)<\/p>\n
polynomial = np.poly1d(coefficients)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u66f2\u7ebf\u62df\u5408\u7ed3\u679c<\/strong><\/h2>\n
plt.scatter(X, y, color='blue', label='Data Points')<\/p>\n
plt.plot(X, polynomial(X), color='red', label='Curve Fit')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Curve Fitting')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4e86\u4e00\u4e2a\u4e94\u6b21\u591a\u9879\u5f0f\u6765\u62df\u5408\u6570\u636e\uff0c\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u5468\u671f\u6027\u6216\u590d\u6742\u6a21\u5f0f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
4.2 \u66f2\u7ebf\u62df\u5408\u7684\u5e94\u7528\u4e0e\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
\u66f2\u7ebf\u62df\u5408\u5e7f\u6cdb\u5e94\u7528\u4e8e\u7269\u7406\u3001\u5316\u5b66\u548c\u751f\u7269\u5b66\u7b49\u9886\u57df\u3002\u7136\u800c\uff0c\u5728\u8fdb\u884c\u66f2\u7ebf\u62df\u5408\u65f6\uff0c\u5e94\u907f\u514d\u8fc7\u62df\u5408\u548c\u6b20\u62df\u5408\u7684\u95ee\u9898\u3002\u8fc7\u62df\u5408\u4f1a\u5bfc\u81f4\u6a21\u578b\u5bf9\u8bad\u7ec3\u6570\u636e\u7684\u8bef\u5dee\u8fc7\u4e8e\u654f\u611f\uff0c\u800c\u6b20\u62df\u5408\u5219\u53ef\u80fd\u65e0\u6cd5\u6355\u6349\u6570\u636e\u7684\u771f\u5b9e\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u6a21\u578b\u8bc4\u4f30\u4e0e\u9009\u62e9<\/p>\n<\/p>\n
\u5728\u62df\u5408\u6563\u70b9\u56fe\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u548c\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u662f\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6a21\u578b\u7684\u9009\u62e9\u5e94\u57fa\u4e8e\u6570\u636e\u7684\u6027\u8d28\u548c\u62df\u5408\u7684\u76ee\u7684\uff0c\u800c\u6027\u80fd\u8bc4\u4f30\u5219\u786e\u4fdd\u6a21\u578b\u7684\u6709\u6548\u6027\u3002<\/p>\n<\/p>\n
5.1 \u4f7f\u7528\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u8bc4\u4f30\u6a21\u578b<\/p>\n<\/p>\n
\u5747\u65b9\u8bef\u5dee\u662f\u8bc4\u4f30\u6a21\u578b\u62df\u5408\u6548\u679c\u7684\u5e38\u7528\u6307\u6807\u4e4b\u4e00\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u9884\u6d4b\u503c\u4e0e\u771f\u5b9e\u503c\u4e4b\u95f4\u7684\u5e73\u65b9\u5dee\u6765\u8861\u91cf\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/strong><\/h2>\n
y_pred = polynomial(X)<\/p>\n
mse = mean_squared_error(y, y_pred)<\/p>\n
print(f'Mean Squared Error: {mse}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8ba1\u7b97MSE\uff0c\u6211\u4eec\u53ef\u4ee5\u91cf\u5316\u6a21\u578b\u9884\u6d4b\u7684\u8bef\u5dee\u5927\u5c0f\uff0c\u4ece\u800c\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
5.2 \u6a21\u578b\u9009\u62e9\u7684\u539f\u5219<\/p>\n<\/p>\n
\u5728\u9009\u62e9\u62df\u5408\u6a21\u578b\u65f6\uff0c\u5e94\u7efc\u5408\u8003\u8651\u6570\u636e\u7684\u7279\u5f81\u548c\u6a21\u578b\u7684\u590d\u6742\u6027\u3002\u7b80\u5355\u7684\u6a21\u578b\uff08\u5982\u7ebf\u6027\u56de\u5f52\uff09\u6613\u4e8e\u89e3\u91ca\u4e14\u8ba1\u7b97\u6548\u7387\u9ad8\uff0c\u9002\u7528\u4e8e\u6570\u636e\u5173\u7cfb\u7b80\u5355\u7684\u60c5\u51b5\uff1b\u800c\u590d\u6742\u7684\u6a21\u578b\uff08\u5982\u975e\u7ebf\u6027\u56de\u5f52\uff09\u5219\u9002\u7528\u4e8e\u6570\u636e\u5173\u7cfb\u590d\u6742\u7684\u60c5\u5f62\uff0c\u4f46\u9700\u8981\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u53c2\u6570\u8c03\u6574\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86Python\u4e2d\u62df\u5408\u6563\u70b9\u56fe\u7684\u51e0\u79cd\u5e38\u7528\u65b9\u6cd5\uff0c\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u591a\u9879\u5f0f\u56de\u5f52\u3001\u975e\u7ebf\u6027\u56de\u5f52\u548c\u66f2\u7ebf\u62df\u5408\u3002\u901a\u8fc7\u7ed3\u5408\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u5e94\u7528\u573a\u666f\uff0c\u5e2e\u52a9\u8bfb\u8005\u7406\u89e3\u4e0d\u540c\u65b9\u6cd5\u7684\u9002\u7528\u6761\u4ef6\u548c\u64cd\u4f5c\u6b65\u9aa4\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u62df\u5408\u65b9\u6cd5\u548c\u6a21\u578b\u81f3\u5173\u91cd\u8981\uff0c\u4e0d\u4ec5\u8981\u8003\u8651\u6570\u636e\u7684\u6027\u8d28\uff0c\u8fd8\u9700\u8bc4\u4f30\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u53ef\u9760\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6563\u70b9\u56fe\u7684\u62df\u5408\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u548cnumpy<\/code>\u5e93\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u8fdb\u884c\u62df\u5408\u3002\u9996\u5148\uff0c\u7528matplotlib<\/code>\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u7136\u540e\u4f7f\u7528numpy.polyfit()<\/code>\u8fdb\u884c\u591a\u9879\u5f0f\u62df\u5408\uff0c\u6700\u540e\u5c06\u62df\u5408\u7ed3\u679c\u7ed8\u5236\u5230\u6563\u70b9\u56fe\u4e0a\u3002\u5177\u4f53\u4ee3\u7801\u793a\u4f8b\u5305\u62ec\u751f\u6210\u968f\u673a\u6570\u636e\u3001\u521b\u5efa\u6563\u70b9\u56fe\u3001\u8fdb\u884c\u7ebf\u6027\u6216\u591a\u9879\u5f0f\u62df\u5408\u7b49\u6b65\u9aa4\u3002<\/p>\n
\u62df\u5408\u6563\u70b9\u56fe\u65f6\uff0c\u4f7f\u7528\u54ea\u79cd\u62df\u5408\u65b9\u6cd5\u6548\u679c\u6700\u4f73\uff1f<\/strong>
\u62df\u5408\u65b9\u6cd5\u7684\u9009\u62e9\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u7ebf\u6027\u62df\u5408\u9002\u7528\u4e8e\u5448\u7ebf\u6027\u5173\u7cfb\u7684\u6570\u636e\uff0c\u800c\u591a\u9879\u5f0f\u62df\u5408\u6216\u5176\u4ed6\u975e\u7ebf\u6027\u62df\u5408\u65b9\u6cd5\uff08\u5982\u6307\u6570\u3001\u5bf9\u6570\u6216\u5e42\u5f8b\u62df\u5408\uff09\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u5173\u7cfb\u3002\u901a\u8fc7\u7ed8\u5236\u6b8b\u5dee\u56fe\u53ef\u4ee5\u5e2e\u52a9\u5224\u65ad\u62df\u5408\u6548\u679c\uff0c\u9009\u62e9\u6700\u9002\u5408\u6570\u636e\u7684\u6a21\u578b\u3002<\/p>\n
\u5982\u4f55\u8bc4\u4f30\u62df\u5408\u6548\u679c\u7684\u597d\u574f\uff1f<\/strong>
\u8bc4\u4f30\u62df\u5408\u6548\u679c\u53ef\u4ee5\u4f7f\u7528R\u00b2\uff08\u51b3\u5b9a\u7cfb\u6570\uff09\u3001\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u7b49\u7edf\u8ba1\u6307\u6807\u3002R\u00b2\u503c\u8d8a\u63a5\u8fd11\uff0c\u8868\u793a\u6a21\u578b\u5bf9\u6570\u636e\u7684\u89e3\u91ca\u80fd\u529b\u8d8a\u5f3a\u3002\u5747\u65b9\u8bef\u5dee\u5219\u53cd\u6620\u4e86\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u901a\u8fc7\u8fd9\u4e9b\u6307\u6807\uff0c\u53ef\u4ee5\u5224\u65ad\u62df\u5408\u7684\u6709\u6548\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d:Python\u62df\u5408\u6563\u70b9\u56fe\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u3001\u591a\u9879\u5f0f\u56de\u5f52\u3001\u975e\u7ebf\u6027\u56de\u5f52\u4ee5\u53ca\u66f2\u7ebf\u62df\u5408\u7b49\u3002 \u5176\u4e2d\uff0c\u7ebf\u6027\u56de\u5f52 […]","protected":false},"author":3,"featured_media":945851,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/945846"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=945846"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/945846\/revisions"}],"predecessor-version":[{"id":945852,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/945846\/revisions\/945852"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/945851"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=945846"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=945846"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=945846"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}