{"id":1184892,"date":"2025-01-15T19:32:10","date_gmt":"2025-01-15T11:32:10","guid":{"rendered":""},"modified":"2025-01-15T19:32:19","modified_gmt":"2025-01-15T11:32:19","slug":"%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e9%a2%84%e6%b5%8bpython-%e4%bb%a3%e7%a0%81","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1184892.html","title":{"rendered":"\u5982\u4f55\u8fdb\u884c\u9884\u6d4bpython \u4ee3\u7801"},"content":{"rendered":"

\"\u5982\u4f55\u8fdb\u884c\u9884\u6d4bpython<\/p>\n

\u5728\u9884\u6d4b\u4efb\u52a1\u4e2d\uff0cPython \u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u62e5\u6709\u4e30\u5bcc\u7684\u5e93\u548c\u6846\u67b6\u6765\u5904\u7406\u5404\u79cd\u9884\u6d4b\u95ee\u9898\u3002\u8981\u8fdb\u884c\u9884\u6d4b Python \u4ee3\u7801\uff0c\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u5982 scikit-learn\u3001TensorFlow\u3001Keras\u3001PyTorch \u7b49<\/strong>\u3002\u5176\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\u662f scikit-learn\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u73b0\u6210\u7684\u7b97\u6cd5\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u5feb\u901f\u4e0a\u624b\u3002\u9996\u5148\u9700\u8981\u51c6\u5907\u6570\u636e\u3001\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u3001\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u6a21\u578b\u8bc4\u4f30\u548c\u9884\u6d4b<\/strong>\u3002\u8ba9\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n

\u5728\u8fdb\u884c\u4efb\u4f55\u9884\u6d4b\u4efb\u52a1\u4e4b\u524d\uff0c\u6570\u636e\u51c6\u5907\u662f\u5173\u952e\u7684\u7b2c\u4e00\u6b65\u3002\u6570\u636e\u901a\u5e38\u4ece\u6570\u636e\u5e93\u3001API\u3001CSV \u6587\u4ef6\u7b49\u591a\u79cd\u6765\u6e90\u83b7\u53d6\u3002<\/p>\n<\/p>\n

1\u3001\u52a0\u8f7d\u6570\u636e<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u6570\u636e\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7 pandas \u5e93\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6 CSV \u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u6570\u636e\u901a\u5e38\u5305\u542b\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\uff0c\u9700\u8981\u8fdb\u884c\u6e05\u6d17\u3002<\/p>\n<\/p>\n
# \u53bb\u9664\u7f3a\u5931\u503c<\/p>\n
data.dropna(inplace=True)<\/p>\n
\u67e5\u770b\u6570\u636e\u63cf\u8ff0<\/strong><\/h2>\n
print(data.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b<\/h3>\n<\/p>\n
\u4e0d\u540c\u7684\u9884\u6d4b\u4efb\u52a1\u53ef\u80fd\u9700\u8981\u4e0d\u540c\u7684\u6a21\u578b\uff0c\u4f8b\u5982\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\u3002\u9009\u62e9\u6a21\u578b\u65f6\u9700\u8981\u8003\u8651\u6570\u636e\u7684\u6027\u8d28\u548c\u76ee\u6807\u3002<\/p>\n<\/p>\n
1\u3001\u5206\u7c7b\u95ee\u9898<\/h4>\n<\/p>\n
\u5982\u679c\u76ee\u6807\u662f\u5206\u7c7b\uff0c\u53ef\u4ee5\u9009\u62e9\u903b\u8f91\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001SVM \u7b49\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u7279\u5f81\u548c\u76ee\u6807<\/strong><\/h2>\n
X = data.drop('target', axis=1)<\/p>\n
y = data['target']<\/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(X, y, test_size=0.3, random_state=42)<\/p>\n
\u9009\u62e9\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668<\/strong><\/h2>\n
model = RandomForestClassifier(n_estimators=100, random_state=42)<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'\u6a21\u578b\u51c6\u786e\u7387: {accuracy}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u56de\u5f52\u95ee\u9898<\/h4>\n<\/p>\n
\u5982\u679c\u76ee\u6807\u662f\u56de\u5f52\uff0c\u53ef\u4ee5\u9009\u62e9\u7ebf\u6027\u56de\u5f52\u3001\u5cad\u56de\u5f52\u3001Lasso \u56de\u5f52\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u7279\u5f81\u548c\u76ee\u6807<\/strong><\/h2>\n
X = data.drop('target', axis=1)<\/p>\n
y = data['target']<\/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(X, y, test_size=0.3, 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
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'\u5747\u65b9\u8bef\u5dee: {mse}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u6570\u636e\u9884\u5904\u7406\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5305\u62ec\u6570\u636e\u6807\u51c6\u5316\u3001\u7279\u5f81\u9009\u62e9\u3001\u964d\u7ef4\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u6807\u51c6\u5316<\/h4>\n<\/p>\n
\u6807\u51c6\u5316\u53ef\u4ee5\u4f7f\u6570\u636e\u7684\u5206\u5e03\u66f4\u5747\u5300\uff0c\u6709\u52a9\u4e8e\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u521b\u5efa\u6807\u51c6\u5316\u5bf9\u8c61<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
\u6807\u51c6\u5316\u6570\u636e<\/strong><\/h2>\n
X_train = scaler.fit_transform(X_train)<\/p>\n
X_test = scaler.transform(X_test)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7279\u5f81\u9009\u62e9<\/h4>\n<\/p>\n
\u7279\u5f81\u9009\u62e9\u53ef\u4ee5\u51cf\u5c11\u7279\u5f81\u6570\u91cf\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6548\u7387\u548c\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.feature_selection import SelectKBest, f_classif<\/p>\n
\u9009\u62e9\u6700\u4f73\u7279\u5f81<\/strong><\/h2>\n
selector = SelectKBest(score_func=f_classif, k=10)<\/p>\n
X_new = selector.fit_transform(X, y)<\/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(X_new, y, test_size=0.3, random_state=42)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6a21\u578b\u8bad\u7ec3<\/h3>\n<\/p>\n
\u8bad\u7ec3\u6a21\u578b\u662f\u6574\u4e2a\u9884\u6d4b\u8fc7\u7a0b\u7684\u6838\u5fc3\u90e8\u5206\uff0c\u6839\u636e\u9009\u62e9\u7684\u6a21\u578b\u548c\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n
1\u3001\u8bad\u7ec3\u5206\u7c7b\u6a21\u578b<\/h4>\n<\/p>\n
# \u9009\u62e9\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668<\/p>\n
model = RandomForestClassifier(n_estimators=100, random_state=42)<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bad\u7ec3\u56de\u5f52\u6a21\u578b<\/h4>\n<\/p>\n
# \u9009\u62e9\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/p>\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
\u4e94\u3001\u6a21\u578b\u8bc4\u4f30\u548c\u9884\u6d4b<\/h3>\n<\/p>\n
\u8bad\u7ec3\u597d\u6a21\u578b\u540e\uff0c\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\uff0c\u4ee5\u786e\u5b9a\u5176\u6027\u80fd\uff0c\u5e76\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
1\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n
\u6a21\u578b\u8bc4\u4f30\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u6307\u6807\uff0c\u5982\u51c6\u786e\u7387\u3001\u5747\u65b9\u8bef\u5dee\u3001\u53ec\u56de\u7387\u3001F1 \u5206\u6570\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score, mean_squared_error, classification_report<\/p>\n
\u5bf9\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'\u6a21\u578b\u51c6\u786e\u7387: {accuracy}')<\/p>\n
print(classification_report(y_test, y_pred))<\/p>\n
\u5bf9\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'\u5747\u65b9\u8bef\u5dee: {mse}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8fdb\u884c\u9884\u6d4b<\/h4>\n<\/p>\n
\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\uff0c\u53ef\u4ee5\u5c06\u65b0\u6570\u636e\u8f93\u5165\u6a21\u578b\uff0c\u5f97\u5230\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n<\/p>\n
# \u65b0\u6570\u636e<\/p>\n
new_data = [[value1, value2, value3, ...]]<\/p>\n
\u6807\u51c6\u5316\u65b0\u6570\u636e<\/strong><\/h2>\n
new_data = scaler.transform(new_data)<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
prediction = model.predict(new_data)<\/p>\n
print(f'\u9884\u6d4b\u7ed3\u679c: {prediction}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6a21\u578b\u4f18\u5316<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u521d\u6b65\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u540e\uff0c\u53ef\u4ee5\u5bf9\u6a21\u578b\u8fdb\u884c\u8fdb\u4e00\u6b65\u4f18\u5316\uff0c\u4ee5\u63d0\u9ad8\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n<\/p>\n
1\u3001\u8d85\u53c2\u6570\u8c03\u6574<\/h4>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\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
    'n_estimators': [50, 100, 200],<\/p>\n
    'max_depth': [None, 10, 20, 30],<\/p>\n
    'min_samples_split': [2, 5, 10]<\/p>\n
}<\/p>\n
\u8fdb\u884c\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
grid_search = GridSearchCV(model, param_grid, cv=5)<\/p>\n
grid_search.fit(X_train, y_train)<\/p>\n
\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
print(f'\u6700\u4f73\u53c2\u6570: {grid_search.best_params_}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4ea4\u53c9\u9a8c\u8bc1<\/h4>\n<\/p>\n
\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u51cf\u5c11\u8fc7\u62df\u5408\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import cross_val_score<\/p>\n
\u8fdb\u884c\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/h2>\n
cv_scores = cross_val_score(model, X, y, cv=5)<\/p>\n
print(f'\u4ea4\u53c9\u9a8c\u8bc1\u8bc4\u5206: {cv_scores}')<\/p>\n
print(f'\u5e73\u5747\u4ea4\u53c9\u9a8c\u8bc1\u8bc4\u5206: {cv_scores.mean()}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u6a21\u578b\u4fdd\u5b58\u548c\u52a0\u8f7d<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u901a\u5e38\u9700\u8981\u4fdd\u5b58\uff0c\u4ee5\u4fbf\u5728\u5c06\u6765\u4f7f\u7528\u3002<\/p>\n<\/p>\n
1\u3001\u4fdd\u5b58\u6a21\u578b<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528 joblib \u6216 pickle \u5e93\u6765\u4fdd\u5b58\u6a21\u578b\u3002<\/p>\n<\/p>\n
import joblib<\/p>\n
\u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
joblib.dump(model, 'model.pkl')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u52a0\u8f7d\u6a21\u578b<\/h4>\n<\/p>\n
\u5728\u9700\u8981\u4f7f\u7528\u6a21\u578b\u65f6\uff0c\u53ef\u4ee5\u5c06\u5176\u52a0\u8f7d\u56de\u6765\u3002<\/p>\n<\/p>\n
# \u52a0\u8f7d\u6a21\u578b<\/p>\n
model = joblib.load('model.pkl')<\/p>\n
\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
prediction = model.predict(new_data)<\/p>\n
print(f'\u9884\u6d4b\u7ed3\u679c: {prediction}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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