我有一个从UCI机器学习库获得的葡萄牙银行数据集，其组织如下：
> head(bank_data)
age job marital education default housing loan contact month day_of_week duration campaign pdays
1 1.53301567694 housemaid married basic.4y no no no telephone may mon 0.01047129616 -0.5659151042 0.1954115279
2 1.62897345569 services married high.school unknown no no telephone may mon -0.42149539806 -0.5659151042 0.1954115279
3 -0.29018211937 services married high.school no yes no telephone may mon -0.12451829578 -0.5659151042 0.1954115279
4 -0.00230878311 admin. married basic.6y no no no telephone may mon -0.41378170709 -0.5659151042 0.1954115279
5 1.53301567694 services married high.school no no yes telephone may mon 0.18788618843 -0.5659151042 0.1954115279
6 0.47748011065 services married basic.9y unknown no no telephone may mon -0.23250996934 -0.5659151042 0.1954115279
previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed targetVar
1 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
2 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
3 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
4 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
5 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
6 -0.3494900415 nonexistent 0.6480843991 0.722713697 0.8864358006 0.7124512301 0.3316758805 no
我正在尝试使用此数据创建一个神经网络，使用nnet包或neuralnet（更容易或最终工作）。在我创建网络之前，我必须首先将所有分类变量转换为二元决策。
有没有一种方法可以“一次性”对所有这些列进行一次性编码？
我尝试使用mltools包：
data <- one_hot(bank_data)
但是这会出现以下错误：
[.data.frame中的错误（dt ,, cols，= = FALSE）：未使用的参数 （有= FALSE）