标签：red selection html style nal square state 技术 mode
1.梯度下降法
先是将需要调整的参数以字典形式存储到param_grid列表中，梯度下降法可调整参数，可参考以下链接
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier
# 导入GridSearch包 from sklearn.model_selection import GridSearchCV sgd = SGDClassifier(max_iter=1000) # 存储需要调整参数 param_grid = [ {‘loss‘: [‘hinge‘, ‘log‘, ‘modified_huber‘, ‘squared_hinge‘,‘perceptron‘], ‘penalty‘: [‘none‘, ‘l2‘, ‘l1‘,‘elasticnet‘]}, ] # 这里使用十折交叉验证，选择评价指标 sgd_grid_search = GridSearchCV(sgd, param_grid, cv=10, scoring=‘precision_macro‘) # 实例化 sgd_grid_search.fit(tfidf_train_features, train_label) # 显示调参过程，取的是十折交叉验证的平均值 for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]): print(mean_score, params)
2.LogisticRegression
逻辑回归参数链接可参考：
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
在选择参数时，需注意
from sklearn.linear_model import LogisticRegression LR = LogisticRegression(max_iter=100,random_state=0,penalty=‘l1‘) param_grid = [ {‘C‘:[1,2,3,4,5], ‘solver‘:[‘liblinear‘,‘saga‘] }, ] LR_grid_search = GridSearchCV(LR, param_grid, cv=10, scoring=‘precision_macro‘) LR_grid_search.fit(tfidf_train_features, train_label) cvres = LR_grid_search.cv_results_ for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]): print(mean_score, params)
其他属性
grid.best_score_ #查看最佳分数(此处为f1_score) grid.best_params_ #查看最佳参数 grid.best_estimator_ # 获取最佳模型 predict_y=best_model.predict(Test_X) # 进行预测 metrics.f1_score(y, predict_y) # 评分
标签：red selection html style nal square state 技术 mode
原文地址：https://www.cnblogs.com/chengziaichiyu/p/10348224.html
