{"id":1083849,"date":"2025-01-08T13:01:58","date_gmt":"2025-01-08T05:01:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1083849.html"},"modified":"2025-01-08T13:02:00","modified_gmt":"2025-01-08T05:02:00","slug":"python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e6%8e%92%e5%88%97%e4%ba%94%e9%a2%84%e6%b5%8b-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1083849.html","title":{"rendered":"python\u5982\u4f55\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b"},"content":{"rendered":"

\"python\u5982\u4f55\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\"<\/p>\n

Python\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\u7684\u65b9\u6cd5\u6709\uff1a\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406\u3001\u7279\u5f81\u5de5\u7a0b\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bad\u7ec3\u3001\u7ed3\u679c\u8bc4\u4f30\u4e0e\u4f18\u5316\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u5c55\u5f00\u4ecb\u7ecd\u5176\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\u2014\u2014\u7279\u5f81\u5de5\u7a0b\u3002<\/p>\n<\/p>\n

\u7279\u5f81\u5de5\u7a0b\u662f\u9884\u6d4b\u6a21\u578b\u4e2d\u4e00\u4e2a\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u901a\u8fc7\u6b63\u786e\u7684\u7279\u5f81\u63d0\u53d6\u548c\u9009\u62e9\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002\u7279\u5f81\u5de5\u7a0b\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u3001\u7279\u5f81\u63d0\u53d6\u3001\u7279\u5f81\u7f29\u653e\u7b49\u6b65\u9aa4\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u53bb\u9664\u5f02\u5e38\u503c\u548c\u7f3a\u5931\u503c\u3002\u63a5\u7740\uff0c\u901a\u8fc7\u7279\u5f81\u9009\u62e9\u65b9\u6cd5\uff08\u5982\u76f8\u5173\u6027\u5206\u6790\uff09\u7b5b\u9009\u51fa\u5bf9\u9884\u6d4b\u6700\u6709\u7528\u7684\u7279\u5f81\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u7279\u5f81\u63d0\u53d6\u65b9\u6cd5\uff08\u5982\u4e3b\u6210\u5206\u5206\u6790\uff09\u5c06\u539f\u59cb\u7279\u5f81\u8f6c\u6362\u4e3a\u65b0\u7684\u7279\u5f81\u3002\u6700\u540e\uff0c\u5bf9\u7279\u5f81\u8fdb\u884c\u6807\u51c6\u5316\u6216\u5f52\u4e00\u5316\u5904\u7406\uff0c\u4f7f\u5176\u7b26\u5408\u6a21\u578b\u7684\u8981\u6c42\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecdPython\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\u7684\u5177\u4f53\u6b65\u9aa4\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6536\u96c6\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n

1\u3001\u6570\u636e\u6536\u96c6<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u6536\u96c6\u6392\u5217\u4e94\u7684\u5386\u53f2\u6570\u636e\u3002\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u4ece\u5f69\u7968\u7f51\u7ad9\u6216\u76f8\u5173\u6570\u636e\u63d0\u4f9b\u5546\u5904\u83b7\u53d6\u3002\u901a\u5e38\uff0c\u6392\u5217\u4e94\u7684\u6570\u636e\u5305\u62ec\u5f00\u5956\u65e5\u671f\u3001\u5f00\u5956\u53f7\u7801\u3001\u548c\u503c\u3001\u5947\u5076\u6bd4\u3001\u5927\u5c0f\u6bd4\u3001\u8fde\u53f7\u7b49\u4fe1\u606f\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('path_to_your_data.csv')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u6536\u96c6\u5230\u6570\u636e\u540e\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u53bb\u9664\u5f02\u5e38\u503c\u548c\u7f3a\u5931\u503c\u3002\u5f02\u5e38\u503c\u53ef\u80fd\u662f\u7531\u4e8e\u6570\u636e\u5f55\u5165\u9519\u8bef\u9020\u6210\u7684\uff0c\u800c\u7f3a\u5931\u503c\u5219\u9700\u8981\u8fdb\u884c\u586b\u8865\u6216\u5220\u9664\u5904\u7406\u3002<\/p>\n<\/p>\n
# \u68c0\u67e5\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u7f3a\u5931\u503c<\/p>\n
print(data.isnull().sum())<\/p>\n
\u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
data = data.dropna()<\/p>\n
\u68c0\u67e5\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c<\/strong><\/h2>\n
print(data.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7279\u5f81\u5de5\u7a0b<\/h3>\n<\/p>\n
1\u3001\u7279\u5f81\u9009\u62e9<\/h4>\n<\/p>\n
\u7279\u5f81\u9009\u62e9\u662f\u6307\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u7b5b\u9009\u51fa\u5bf9\u9884\u6d4b\u6700\u6709\u7528\u7684\u7279\u5f81\u3002\u53ef\u4ee5\u901a\u8fc7\u76f8\u5173\u6027\u5206\u6790\u3001\u7279\u5f81\u91cd\u8981\u6027\u7b49\u65b9\u6cd5\u6765\u8fdb\u884c\u7279\u5f81\u9009\u62e9\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8ba1\u7b97\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u5173\u6027<\/strong><\/h2>\n
correlation_matrix = data.corr()<\/p>\n
\u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7279\u5f81\u63d0\u53d6<\/h4>\n<\/p>\n
\u7279\u5f81\u63d0\u53d6\u662f\u5c06\u539f\u59cb\u7279\u5f81\u8f6c\u6362\u4e3a\u65b0\u7684\u7279\u5f81\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u3001\u7ebf\u6027\u5224\u522b\u5206\u6790\uff08LDA\uff09\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.decomposition import PCA<\/p>\n
\u63d0\u53d6\u7279\u5f81\u5217<\/strong><\/h2>\n
features = data.drop(['target'], axis=1)<\/p>\n
\u5e94\u7528PCA\u8fdb\u884c\u7279\u5f81\u63d0\u53d6<\/strong><\/h2>\n
pca = PCA(n_components=5)<\/p>\n
principal_components = pca.fit_transform(features)<\/p>\n
\u521b\u5efa\u65b0\u7684DataFrame<\/strong><\/h2>\n
principal_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7279\u5f81\u7f29\u653e<\/h4>\n<\/p>\n
\u7279\u5f81\u7f29\u653e\u662f\u5bf9\u7279\u5f81\u8fdb\u884c\u6807\u51c6\u5316\u6216\u5f52\u4e00\u5316\u5904\u7406\uff0c\u4f7f\u5176\u7b26\u5408\u6a21\u578b\u7684\u8981\u6c42\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u6807\u51c6\u5316\u7279\u5f81<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
scaled_features = scaler.fit_transform(features)<\/p>\n
\u521b\u5efa\u65b0\u7684DataFrame<\/strong><\/h2>\n
scaled_df = pd.DataFrame(data=scaled_features, columns=features.columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bad\u7ec3<\/h3>\n<\/p>\n
1\u3001\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b<\/h4>\n<\/p>\n
\u5728\u7279\u5f81\u5de5\u7a0b\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u3002\u5e38\u7528\u7684\u6a21\u578b\u6709\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u795e\u7ecf\u7f51\u7edc\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(scaled_features, data['target'], test_size=0.2, random_state=42)<\/p>\n
\u9009\u62e9\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n
\u5728\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\uff0c\u4ee5\u786e\u5b9a\u5176\u9884\u6d4b\u80fd\u529b\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u6709\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3001\u51b3\u5b9a\u7cfb\u6570\uff08R^2\uff09\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.metrics import mean_squared_error, r2_score<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee\u548c\u51b3\u5b9a\u7cfb\u6570<\/strong><\/h2>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
r2 = r2_score(y_test, y_pred)<\/p>\n
print(f'Mean Squared Error: {mse}')<\/p>\n
print(f'R^2 Score: {r2}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed3\u679c\u8bc4\u4f30\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n
1\u3001\u4ea4\u53c9\u9a8c\u8bc1<\/h4>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u6a21\u578b\u7684\u7a33\u5b9a\u6027\u548c\u6cdb\u5316\u80fd\u529b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u65b9\u6cd5\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709K\u6298\u4ea4\u53c9\u9a8c\u8bc1\u3001\u7559\u4e00\u6cd5\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import cross_val_score<\/p>\n
\u8fdb\u884cK\u6298\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/h2>\n
cv_scores = cross_val_score(model, scaled_features, data['target'], cv=5)<\/p>\n
print(f'Cross-Validation Scores: {cv_scores}')<\/p>\n
print(f'Mean CV Score: {cv_scores.mean()}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6a21\u578b\u4f18\u5316<\/h4>\n<\/p>\n
\u5728\u8bc4\u4f30\u6a21\u578b\u540e\uff0c\u5982\u679c\u53d1\u73b0\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u4e0d\u7406\u60f3\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u53c2\u6570\u3001\u9009\u62e9\u5176\u4ed6\u6a21\u578b\u3001\u589e\u52a0\u66f4\u591a\u7684\u7279\u5f81\u7b49\u65b9\u6cd5\u5bf9\u6a21\u578b\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import GridSearchCV<\/p>\n
\u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
param_grid = {<\/p>\n
    'fit_intercept': [True, False],<\/p>\n
    'normalize': [True, False]<\/p>\n
}<\/p>\n
\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u8fdb\u884c\u53c2\u6570\u8c03\u4f18<\/strong><\/h2>\n
grid_search = GridSearchCV(model, param_grid, cv=5)<\/p>\n
grid_search.fit(X_train, y_train)<\/p>\n
\u8f93\u51fa\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
print(f'Best Parameters: {grid_search.best_params_}')<\/p>\n
\u4f7f\u7528\u6700\u4f73\u53c2\u6570\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
best_model = grid_search.best_estimator_<\/p>\n
best_model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8fdb\u884c\u6392\u5217\u4e94\u7684\u9884\u6d4b\u3002\u9996\u5148\uff0c\u9700\u8981\u6536\u96c6\u5e76\u6e05\u6d17\u6570\u636e\u3002\u63a5\u7740\uff0c\u901a\u8fc7\u7279\u5f81\u5de5\u7a0b\u63d0\u53d6\u548c\u9009\u62e9\u6709\u7528\u7684\u7279\u5f81\u3002\u7136\u540e\uff0c\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\u548c\u4f18\u5316\u3002\u6700\u540e\uff0c\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u548c\u53c2\u6570\u8c03\u4f18\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u548c\u6280\u5de7\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6392\u5217\u4e94\u7684\u9884\u6d4b\u6a21\u578b\u6784\u5efa\uff1f<\/strong>
\u6784\u5efa\u6392\u5217\u4e94\u7684\u9884\u6d4b\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u673a\u5668\u5b66\u4e60<\/a>\u548c\u7edf\u8ba1\u5206\u6790\u7684\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u9700\u8981\u6536\u96c6\u5386\u53f2\u6570\u636e\uff0c\u5305\u62ec\u6bcf\u671f\u7684\u5f00\u5956\u53f7\u7801\u53ca\u5176\u51fa\u73b0\u9891\u7387\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5982\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u6216\u795e\u7ecf\u7f51\u7edc\uff0c\u5bf9\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u3002\u901a\u8fc7\u5bf9\u5386\u53f2\u6570\u636e\u7684\u6a21\u5f0f\u8bc6\u522b\u548c\u8d8b\u52bf\u5206\u6790\uff0c\u6a21\u578b\u5c06\u80fd\u591f\u9884\u6d4b\u672a\u6765\u7684\u5f00\u5956\u53f7\u7801\u3002\u8bb0\u5f97\u5728\u6a21\u578b\u8bad\u7ec3\u548c\u6d4b\u8bd5\u65f6\uff0c\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n
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