在使用 xgboost 训练数据和检验模型时,如果测试集的特征最大编号数大于训练集的特征最大编号数,在测试时,会报 ValueError: feature_names mismatch 和 training data did not have the following fields 错误。 1具体报错信息 2解决问题方法 2.1libsvm 样本都追加最大特征编号 具体报错信息 Traceback (most recent ...
I'm getting ValueError: feature_names mismatch while training xgboost with sparse matrices in python. The xgboost version is latest from git. Older versions don't give this error. Error is returned during prediction time. code from scipy...
xgboost\core.py", line 869, in feature_names raise ValueError('feature_names may not contain [, ] or <') ValueError: feature_names may not contain [, ] or < 解决思路 错误地址:xgboost\core.py", line 869, 值错误:feature_names中不能包含[,]或<的符号 解决方法 '添加以下三行解决问题' im...
解决问题 ValueError: feature_names mismatch: ['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'black', 'lstat', 'crim_(0, 10_', 'crim_(10, 20_', 'crim_(20, 100_', 'zn_(-1, 5_', 'zn_(5, 18_', 'zn_(18, 20_', '...
ValueError: feature_names mismatch:["f1","f2","f3","f4","f5","f6", ……"f60123", ] 原因分析 - 上网百度,大多回答都是: 训练集和测试集的列名不一致 训练集和测试集的列名顺序不一致 如果有以上两种的可以尝试进行修改 - 笔者遇到的是另外一种: ...
Reward R is the agent's action compared to the original Label, where a match is rewarded with 1, and a mismatch receives no reward (i.e., R=0or1). The Q function estimates the value of each state and is continuously updated using the Agent process loss function. The DQN algorithm ...