小编zha*_*usm的帖子

例如,当我在R(pROC包)中使用multiclass.roc函数时,我通过随机林训练了一个数据集,这是我的代码:

# randomForest & pROC packages should be installed:
# install.packages(c('randomForest', 'pROC'))
data(iris)
library(randomForest)
library(pROC)
set.seed(1000)
# 3-class in response variable
rf = randomForest(Species~., data = iris, ntree = 100)
# predict(.., type = 'prob') returns a probability matrix
multiclass.roc(iris$Species, predict(rf, iris, type = 'prob'))

结果是:

Call:
multiclass.roc.default(response = iris$Species, predictor = predict(rf,     
iris, type = "prob"))
Data: predict(rf, iris, type = "prob") with 3 levels of iris$Species: setosa,   
versicolor, virginica.
Multi-class area under the curve: 0.5142

这是正确的吗？谢谢!!!

"pROC"参考:http …

我想计算 Maxima 中 x=pi/4 处 tan(x) 的七阶导数：

f(x) := diff(tan(x), x, 7);
f(%pi / 4);

但我无法得到结果。有想法吗？

我有一个数据框(名为'mdf'),其中包含两列.基本信息如下:

> head(mdf); tail(mdf)
  Country Rank
1     ABW  161
2     AFG  105
3     AGO   60
4     ALB  125
5     ARE   32
6     ARG   26
    Country Rank
184     WSM  181
185     YEM   90
186     ZAF   28
187     ZAR  112
188     ZMB  104
189     ZWE  134
> str(mdf)
'data.frame':   189 obs. of  2 variables:
 $ Country: Factor w/ 229 levels "","ABW","ADO",..: 2 4 5 6 7 8 9 11 12 13 ...
 $ Rank   : Factor w/ 195 levels "",".. Not available. …