# howork_scikit_learn.py# 1. 数据准备importpandasaspdfromsklearn.model_selectionimporttrain_test_split data=pd.read_csv('dataset.csv')X=data.drop('target',axis=1)# 特征y=data['target']# 标签# 2. 划分训练集和测试集# X_train, X_test, y_train, y_test = train_test_split(# X, y, test_size=0.2, random_state=42# )y_binary=(y>y.mean()).astype(int)# 根据均值分割X_train,X_test,y_train,y_test=train_test_split(X,y_binary,test_size=0.2,random_state=42)# 3. 特征工程（标准化）fromsklearn.preprocessingimportStandardScaler scaler=StandardScaler()X_train_scaled=scaler.fit_transform(X_train)X_test_scaled=scaler.transform(X_test)# 4. 选择模型并训练fromsklearn.ensembleimportRandomForestClassifier model=RandomForestClassifier(n_estimators=100,random_state=42)model.fit(X_train_scaled,y_train)# 5. 预测与评估fromsklearn.metricsimportaccuracy_score predictions=model.predict(X_test_scaled)accuracy=accuracy_score(y_test,predictions)print(f"模型准确率:{accuracy:.2f}")# 6. 模型保存importjoblib joblib.dump(model,'trained_model.pkl')运行结果：
(ai_env)$ python3 howork_scikit_learn.py 模型准确率: 0.91(ai_env)$
