{"id":990156,"date":"2024-12-27T08:17:47","date_gmt":"2024-12-27T00:17:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/990156.html"},"modified":"2024-12-27T08:17:49","modified_gmt":"2024-12-27T00:17:49","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e6%8b%9f%e5%90%88%e8%af%af%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/990156.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u62df\u5408\u8bef\u5dee"},"content":{"rendered":"

\"python\u5982\u4f55\u7ed8\u5236\u62df\u5408\u8bef\u5dee\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u62df\u5408\u8bef\u5dee\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u7ed8\u5236\u6b8b\u5dee\u56fe\u3001\u901a\u8fc7Seaborn\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790\u3001\u5229\u7528Scikit-learn\u8fdb\u884c\u9884\u6d4b\u4e0e\u8bef\u5dee\u8ba1\u7b97<\/strong>\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u6b8b\u5dee\u56fe\u3002<\/p>\n<\/p>\n

\u5728\u6570\u636e\u5206\u6790\u548c\u6a21\u578b\u62df\u5408\u8fc7\u7a0b\u4e2d\uff0c\u4e86\u89e3\u6a21\u578b\u7684\u62df\u5408\u8bef\u5dee\u662f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u6b8b\u5dee\u56fe\u662f\u5206\u6790\u62df\u5408\u8bef\u5dee\u7684\u4e00\u79cd\u5e38\u7528\u5de5\u5177\uff0c\u5b83\u901a\u8fc7\u5c55\u793a\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u5e2e\u52a9\u6211\u4eec\u5224\u65ad\u6a21\u578b\u7684\u62df\u5408\u8d28\u91cf\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u6b8b\u5dee\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u9996\u5148\uff0c\u8ba1\u7b97\u6a21\u578b\u7684\u9884\u6d4b\u503c\u548c\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u6b8b\u5dee\uff1b\u5176\u6b21\uff0c\u4f7f\u7528Matplotlib\u7ed8\u5236\u6b8b\u5dee\u968f\u9884\u6d4b\u503c\u53d8\u5316\u7684\u6563\u70b9\u56fe\u3002\u901a\u8fc7\u89c2\u5bdf\u6b8b\u5dee\u7684\u5206\u5e03\uff0c\u53ef\u4ee5\u5224\u65ad\u6a21\u578b\u662f\u5426\u5b58\u5728\u7cfb\u7edf\u6027\u8bef\u5dee\u3002\u5982\u679c\u6b8b\u5dee\u5728\u96f6\u9644\u8fd1\u968f\u673a\u5206\u5e03\uff0c\u8bf4\u660e\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\u8f83\u597d\uff1b\u5982\u679c\u51fa\u73b0\u660e\u663e\u7684\u6a21\u5f0f\u6216\u8d8b\u52bf\uff0c\u5219\u53ef\u80fd\u9700\u8981\u6539\u8fdb\u6a21\u578b\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63a2\u8ba8\u5176\u4ed6\u7ed8\u5236\u62df\u5408\u8bef\u5dee\u7684\u65b9\u6cd5\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528MATPLOTLIB\u7ed8\u5236\u6b8b\u5dee\u56fe<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u4ee3\u7801\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6b8b\u5dee\u56fe\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5\u548c\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\u548c\u5176\u4ed6\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u751f\u6210\u6a21\u62df\u6570\u636e<\/h4>\n<\/p>\n
\u4e3a\u4e86\u6f14\u793a\uff0c\u6211\u4eec\u5148\u751f\u6210\u4e00\u4e9b\u6a21\u62df\u6570\u636e\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u968f\u673a\u6570\u636e<\/p>\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
<\/code><\/pre>\n<\/p>\n
3. \u62df\u5408\u6a21\u578b\u5e76\u8ba1\u7b97\u6b8b\u5dee<\/h4>\n<\/p>\n
\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u62df\u5408\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u6b8b\u5dee\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/p>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
\u9884\u6d4b\u503c<\/strong><\/h2>\n
y_pred = model.predict(X)<\/p>\n
\u8ba1\u7b97\u6b8b\u5dee<\/strong><\/h2>\n
residuals = y - y_pred<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u7ed8\u5236\u6b8b\u5dee\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528Matplotlib\u7ed8\u5236\u6b8b\u5dee\u56fe\uff1a<\/p>\n<\/p>\n
plt.scatter(y_pred, residuals)<\/p>\n
plt.hlines(y=0, xmin=y_pred.min(), xmax=y_pred.max(), linestyles='dashed')<\/p>\n
plt.xlabel('Predicted Values')<\/p>\n
plt.ylabel('Residuals')<\/p>\n
plt.title('Residuals Plot')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u89c2\u5bdf\u6b8b\u5dee\u56fe\uff0c\u5224\u65ad\u6b8b\u5dee\u662f\u5426\u5728\u96f6\u9644\u8fd1\u968f\u673a\u5206\u5e03\uff0c\u4ee5\u8bc4\u4f30\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528SEABORN\u8fdb\u884c\u6b8b\u5dee\u5206\u6790<\/h3>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/h4>\n<\/p>\n
\u786e\u4fdd\u5b89\u88c5\u4e86Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Seaborn\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528residplot\u7ed8\u5236\u6b8b\u5dee\u56fe<\/h4>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86residplot<\/code>\u51fd\u6570\uff0c\u4e13\u95e8\u7528\u4e8e\u7ed8\u5236\u6b8b\u5dee\u56fe\uff1a<\/p>\n<\/p>\n
sns.residplot(x=y_pred.flatten(), y=residuals.flatten(), lowess=True)<\/p>\n
plt.xlabel('Predicted Values')<\/p>\n
plt.ylabel('Residuals')<\/p>\n
plt.title('Residuals Plot using Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
Seaborn\u7684residplot<\/code>\u4e0d\u4ec5\u80fd\u7ed8\u5236\u6b8b\u5dee\u56fe\uff0c\u8fd8\u80fd\u901a\u8fc7lowess<\/code>\u53c2\u6570\u6dfb\u52a0\u5c40\u90e8\u52a0\u6743\u56de\u5f52\u7ebf\uff0c\u4ee5\u66f4\u6e05\u6670\u5730\u5c55\u793a\u6b8b\u5dee\u7684\u5206\u5e03\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5229\u7528SCIKIT-LEARN\u8fdb\u884c\u9884\u6d4b\u4e0e\u8bef\u5dee\u8ba1\u7b97<\/h3>\n<\/p>\n
Scikit-learn\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6a21\u578b\u548c\u5de5\u5177\u6765\u5904\u7406\u9884\u6d4b\u548c\u8bef\u5dee\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Scikit-learn\u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/h4>\n<\/p>\n
\u524d\u9762\u7684\u4f8b\u5b50\u5df2\u7ecf\u6f14\u793a\u4e86\u5982\u4f55\u4f7f\u7528Scikit-learn\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\uff0c\u8fd9\u91cc\u4e0d\u518d\u8d58\u8ff0\u3002<\/p>\n<\/p>\n
2. \u8ba1\u7b97\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09<\/h4>\n<\/p>\n
\u9664\u4e86\u6b8b\u5dee\u56fe\uff0c\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u4e5f\u662f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6307\u6807\uff1a<\/p>\n<\/p>\n
mse = mean_squared_error(y, y_pred)<\/p>\n
print(f"Mean Squared Error: {mse}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f83\u5c0f\u7684MSE\u8868\u793a\u6a21\u578b\u5bf9\u6570\u636e\u7684\u62df\u5408\u6548\u679c\u66f4\u597d\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5176\u4ed6\u8bef\u5dee\u53ef\u89c6\u5316\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u6b8b\u5dee\u56fe\uff0c\u8fd8\u6709\u5176\u4ed6\u53ef\u89c6\u5316\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u5206\u6790\u62df\u5408\u8bef\u5dee\u3002<\/p>\n<\/p>\n
1. \u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u5bf9\u6bd4\u56fe<\/h4>\n<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u7684\u5bf9\u6bd4\u56fe\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\uff1a<\/p>\n<\/p>\n
plt.scatter(X, y, label='Actual')<\/p>\n
plt.scatter(X, y_pred, label='Predicted', alpha=0.7)<\/p>\n
plt.xlabel('Feature')<\/p>\n
plt.ylabel('Target')<\/p>\n
plt.title('Actual vs Predicted')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u89c2\u5bdf\u5b9e\u9645\u503c\u548c\u9884\u6d4b\u503c\u7684\u5206\u5e03\uff0c\u53ef\u4ee5\u5224\u65ad\u6a21\u578b\u662f\u5426\u5b58\u5728\u7cfb\u7edf\u6027\u504f\u5dee\u3002<\/p>\n<\/p>\n
2. \u6b8b\u5dee\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u6b8b\u5dee\u76f4\u65b9\u56fe\u53ef\u4ee5\u5e2e\u52a9\u5206\u6790\u6b8b\u5dee\u7684\u5206\u5e03\u7279\u6027\uff1a<\/p>\n<\/p>\n
plt.hist(residuals, bins=20)<\/p>\n
plt.xlabel('Residuals')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram of Residuals')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5982\u679c\u6b8b\u5dee\u5448\u6b63\u6001\u5206\u5e03\uff0c\u8bf4\u660e\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\u8f83\u597d\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u548c\u6a21\u578b\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u4e86\u89e3\u548c\u5206\u6790\u62df\u5408\u8bef\u5dee\u662f\u63d0\u9ad8\u6a21\u578b\u8d28\u91cf\u7684\u91cd\u8981\u73af\u8282\u3002\u901a\u8fc7\u7ed8\u5236\u6b8b\u5dee\u56fe\u3001\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee\u3001\u4ee5\u53ca\u5176\u4ed6\u53ef\u89c6\u5316\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6a21\u578b\u7684\u6027\u80fd<\/strong>\u3002\u5728\u5b9e\u8df5\u4e2d\uff0c\u5efa\u8bae\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u8fdb\u884c\u8bef\u5dee\u5206\u6790\uff0c\u4ee5\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u7684\u62df\u5408\u6548\u679c\u3002<\/p>\n<\/p>\n
\u6b64\u5916\uff0c\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u548c\u7279\u5f81\u5de5\u7a0b\u65b9\u6cd5\u4e5f\u662f\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u7684\u5173\u952e\u3002\u5728\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u65f6\uff0c\u5e94\u6839\u636e\u5177\u4f53\u7684\u6570\u636e\u7279\u6027\u548c\u95ee\u9898\u80cc\u666f\u8fdb\u884c\u5408\u7406\u8c03\u6574\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u62df\u5408\u8bef\u5dee\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u89c1\u7684\u62df\u5408\u8bef\u5dee\u8ba1\u7b97\u65b9\u6cd5\u5305\u62ec\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684\u51fd\u6570\u6765\u8ba1\u7b97\u8fd9\u4e9b\u8bef\u5dee\u3002\u4f8b\u5982\uff0cMSE\u53ef\u4ee5\u901a\u8fc7numpy.mean((y_true - y_pred) ** 2)<\/code>\u6765\u5b9e\u73b0\uff0c\u5176\u4e2dy_true<\/code>\u662f\u5b9e\u9645\u503c\uff0cy_pred<\/code>\u662f\u9884\u6d4b\u503c\u3002\u4f7f\u7528Scikit-learn\u5e93\u4e5f\u80fd\u65b9\u4fbf\u5730\u8ba1\u7b97\u8fd9\u4e9b\u8bef\u5dee\uff0c\u65b9\u6cd5\u662fmean_squared_error<\/code>\u548cmean_absolute_error<\/code>\u51fd\u6570\u3002<\/p>\n
\u6709\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u62df\u5408\u8bef\u5dee\u56fe\uff1f<\/strong>
\u591a\u4e2aPython\u5e93\u652f\u6301\u7ed8\u5236\u62df\u5408\u8bef\u5dee\u56fe\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cSeaborn\u3002Matplotlib\u53ef\u4ee5\u521b\u5efa\u57fa\u672c\u7684\u6563\u70b9\u56fe\u548c\u7ebf\u56fe\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u56fe\u5f62\u6837\u5f0f\u3002\u901a\u8fc7\u5c06\u62df\u5408\u503c\u4e0e\u5b9e\u9645\u503c\u8fdb\u884c\u5bf9\u6bd4\uff0c\u7ed8\u5236\u8bef\u5dee\u56fe\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u6a21\u578b\u7684\u8868\u73b0\u548c\u8bef\u5dee\u5206\u5e03\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u4f18\u5316\u62df\u5408\u6a21\u578b\u4ee5\u51cf\u5c11\u8bef\u5dee\uff1f<\/strong>
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