如何解决错误:总数分类模型使用堆叠集成时达到ITERATIONS REACH LIMIT的限制
我想使用堆叠集成模型构建分类模型。
这是我的代码:
level0 = list()
level0.append(('log',LogisticRegression()))
level0.append(('rf',RandomForestClassifier(n_estimators=600,class_weight='balanced')))
level0.append(('xgb',XGBClassifier(scale_pos_weight = sum_neg/sum_pos)))
# define meta learner model
level1 = LogisticRegression(solver='lbfgs',max_iter=10000)
# define the stacking ensemble
model = StackingClassifier(estimators=level0,final_estimator=level1,cv=5)
model.fit(X_train,y_train.ravel())
y_pred = model.predict(X_test)
这是错误:
764: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
我将max_iter增加到10000,但仍然会产生此错误。
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。