加载caret包时,会出现以下错误。
> library(caret)
Error: package or namespace load failed for ‘caret’:
object ‘warnErrList’ is not exported by 'namespace:utils'
即使我尝试安装带有所有依赖项的“插入符号”,它仍然显示: object‘warnErrList’不是由‘命名空间:utils’导出的。
我在用R版本3.2.2革命R企业(2015-08-14)
当我试图安装library(caret)时,我得到了这个错误
library(caret)
Loading required package: ggplot2
Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
there is no package called ‘Rcpp’
In addition: Warning messages:
1: package ‘caret’ was built und
我在R (2.14.2)中成功安装了'adabag‘包,但当我加载它时,我得到了以下错误:
Loading required package: caret
Error : object 'parallelplot' not found whilst loading namespace 'caret'
Error: package ‘caret’ could not be loaded
我不确定'parallelplot‘是不是一个包。我也试图安装'parallelplot‘包,但它在R版本2.14.2上不可用。有人能帮我解决这个问题吗?
我正在尝试安装R软件包caret
这给了我ERROR: dependencies ‘ggplot2’, ‘reshape2’, ‘BradleyTerry2’ are not available for package ‘caret’
我试着分别安装其中的每一个,这再次显示了正在进行的安装,并以installation of package ‘X’ had non-zero exit status的消息结尾
1: In install.packages("caret") :
installation of package ‘minqa’ had non-zero exit
下面是我找到的downSample caret函数。
downSample <- function(x, y, list = FALSE, yname = "Class")
{
xc <- class(x)
if(!is.data.frame(x)) x <- as.data.frame(x)
if(!is.factor(y))
{
warning("Down-sampling requires a factor variable as the response. The original dat
我在尝试安装脱字符软件包时遇到了这个错误:
ERROR: compilation failed for package ‘ddalpha’
* removing ‘/home/rspark/R/x86_64-redhat-linux-gnu-library/3.3/ddalpha’
Warning in install.packages :
installation of package ‘ddalpha’ had non-zero exit status
ERROR: dependency ‘ddalpha’ is not available for package ‘recipes’
library(caret)
Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) :
object 'vI' not found
In addition: Warning message:
package ‘caret’ was built under R version 3.3.3
Error: package or namespace load failed for ‘caret’
我尝试了从重新安装R到删除所有库文件夹的所有方法,但都没有帮助。
我正在尝试安装library(caret),但在juypter notebook中不断收到此错误。我还尝试了install.packages("ggplot2)和library(ggplot2),但都不起作用。也是install.packages('caret', dependencies = TRUE),但是同样的错误
library(e1071)
library(caret)
library(kknn)
library(AUC)
library(MASS)
"package 'ggplot2' was built under R vers
我一直在使用forestFloor包来可视化随机森林模型结构。在我尝试对caret包中的随机林输出使用forestFloor()之前,一切都很顺利。我需要为我的数据集使用caret而不是randomForest,因为我有严重不平衡的类,所以使用SMOTE采样策略。我修复了一些问题,将keep.inbag=TRUE和keep.forest=TRUE传递给caret::train,然后找到隐藏在train类对象$finalModel中的randomForest对象。
我仍然得到错误:Error in eval(substitute(expr), envir, enclos) : index out
我有一个函数downsample_vec,它接受一个Vec,并根据它们的位置删除它的一些值。我在注释正确的特性时遇到了麻烦(我只需要Clone和Index,但无法使其工作),所以我决定使用self来看看能否说服编译器做出正确的推断:
impl Vec<IndexMut<usize>> {
fn downsample<usize>(&mut self, factor: usize) {
let len = self.len();
if factor > len {
self.clea
我有一个Python代码,可以很好地在数据集上执行k折叠简历。我的Python代码如下所示:
import pandas
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVR
from sklearn.utils import shuffle
# Load the dataset.
dataset = pandas.read_csv('values.csv')
当我尝试在插入符号中运行createDataPartition时,我得到了以下错误。
Error in createDataPartition(data1, p = 0.8, list = FALSE) :
y must have at least 2 data points
我昨晚运行了完全相同的代码,没有任何错误。有什么想法吗?
predictors<- with(df, data.frame(xvar, xvar, xvar, xvar))
data1<-with(dfu2, data.frame(data1))
library(caret)
set.seed(1)
t
在install.packages("caret")出现以下错误后:
Installing package into ‘...../R/win-library/3.4’
(as ‘lib’ is unspecified)
also installing the dependencies ‘rlang’, ‘recipes’
There are binary versions available but the source versions are later:
binary source needs_compilation
rlang 0.3.4
在我新安装的R上运行了一个data.table测试,得到了以下错误:
> test.data.table()
Error in eval(exprs[i], envir) :
10 errors out of 8403 (lastID=1887, endian==little, sizeof(long
double)==16, sizeof(pointer)==8) in inst/tests/tests.Rraw on Tue Apr 03
11:28:16 2018. Search tests.Rraw for test numbers: 546, 1693.4, 1693
请看这段代码:
sig_array=[]
...
for i in range (0, 2):
....
temp=[]
for k in range (0, len (sig)):
#print (k)
temp.append(downsample(sig[k],sampl, new_freq))
sig_array.append(temp)
换句话说,temp是一个数组列表(我的downsample函数,顾名思义,就是返回一个数组),然后temp将被聚集,因此它将是一个数组列表!