在尝试使用插入符号包训练随机森林模型时,我注意到执行时间莫名其妙:
> set.seed = 1;
> n = 500;
> m = 30;
> x = matrix(rnorm(n * m), nrow = n);
> y = factor(sample.int(2, n, replace = T), labels = c("yes", "no"))
> require(caret);
> require(randomForest);
> print(system.time({rf <- randomForest(x, y);}));
user system elapsed
0.99 0.00 0.98
> print(system.time({rfmod <- train(x = x, y = y,
+ method = "rf",
+ metric = "Accuracy",
+ trControl = trainControl(classProbs = T)
+ );}));
user system elapsed
95.83 0.71 97.26 …我想比较标准的神经网络方法发挥到了极致学习机分类器（基于ROC指标），使用方法"nnet"，并"elm"在R包caret。对于nnet，一切正常，但是使用时method = "elm"出现以下错误：
Error in evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels, :
train()'s use of ROC codes requires class probabilities. See the classProbs option of trainControl()
In addition: Warning messages:
1: In train.default(x, y, weights = w, ...) :
At least one of the class levels are not valid R variables names; This may cause errors if class probabilities are generated because the variables names will be converted …主要问题:
在阅读文档和谷歌搜索之后,我仍然难以确定预先定义重采样指数的情况,例如:
resamples <- createResample(classVector_training, times = 500, list=TRUE)
或预定义的种子,如:
seeds <- vector(mode = "list", length = 501) #length is = (n_repeats*nresampling)+1
for(i in 1:501) seeds[[i]]<- sample.int(n=1000, 1)
我的计划是通过doParallel软件包使用并行处理来训练一堆不同的可重现模型.由于已经设置了种子,是否不需要预定义重新采样？我是否需要以上述方式预定义种子,而不是在trainControl对象中设置seeds = NULL,因为我打算使用并行处理？是否有任何理由预先定义索引和种子,因为我通过搜索谷歌至少看过一次？什么是使用indexOut的原因？
问题:
到目前为止,我已经设法为RF运行良好的列车:
rfControl <- trainControl(method="oob", number = 500, p = 0.7, returnData=TRUE, returnResamp = "all", savePredictions=TRUE, classProbs = TRUE, summaryFunction = twoClassSummary, allowParallel=TRUE)
mtryGrid <- expand.grid(mtry = 9480^0.5) #set mtry parameter to the square root of the number of variables
rfTrain <- train(x = training, y = classVector_training, method …我收到以下错误,我不知道可能出了什么问题.我正在使用R Studio和3.1.3版本的R for Windows 8.1并使用Caret包进行数据挖掘.
我有以下培训数据:
str(training)
'data.frame': 212300 obs. of 21 variables:
$ FL_DATE_MDD_MMDD : int 101 101 101 101 101 101 101 101 101 101 ...
$ FL_DATE : int 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 ...
$ UNIQUE_CARRIER : Factor w/ 13 levels "9E","AA","AS",..: 11 10 2 5 8 9 11 10 10 10 ...
$ DEST : Factor w/ 150 levels "ABE","ABQ","ALB",..: 111 70 82 8 8 31 110 44 53 80 …您好,并提前致谢.我正在使用caret从nnet包中交叉验证神经网络.在函数的method参数中,trainControl我可以指定交叉验证类型,但所有这些都随机选择观察结果以进行交叉验证.无论如何,我可以使用插入符号通过ID或硬编码参数来交叉验证我的数据中的特定观察结果吗？例如,这是我当前的代码:
library(nnet)
library(caret)
library(datasets)
data(iris)
train.control <- trainControl(
method = "repeatedcv"
, number = 4
, repeats = 10
, verboseIter = T
, returnData = T
, savePredictions = T
)
tune.grid <- expand.grid(
size = c(2,4,6,8)
,decay = 2^(-3:1)
)
nnet.train <- train(
x = iris[,1:4]
, y = iris[,5]
, method = "nnet"
, preProcess = c("center","scale")
, metric = "Accuracy"
, trControl = train.control
, tuneGrid = tune.grid …我正在使用插入符包训练R中的模型:
ctrl <- trainControl(method = "repeatedcv", repeats = 3, summaryFunction = twoClassSummary)
logitBoostFit <- train(LoanStatus~., credit, method = "LogitBoost", family=binomial, preProcess=c("center", "scale", "pca"),
trControl = ctrl)
我收到以下警告:
Warning message:
In train.default(x, y, weights = w, ...): The metric "Accuracy" was not in the result set. ROC will be used instead.Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
Something is wrong; all the ROC metric …我正在构建一个CART模型,我正在尝试调整rpart-CP和Maxdepth的2个参数.虽然Caret软件包一次适用于一个参数,但当两者都使用时它会不断抛出错误而我无法弄清楚为什么
library(caret)
data(iris)
tc <- trainControl("cv",10)
rpart.grid <- expand.grid(cp=seq(0,0.1,0.01), minsplit=c(10,20))
train(Petal.Width ~ Petal.Length + Sepal.Width + Sepal.Length, data=iris, method="rpart",
trControl=tc, tuneGrid=rpart.grid)
我收到以下错误:
Error in train.default(x, y, weights = w, ...) :
The tuning parameter grid should have columns cp
在插入符号中使用glmnet时出现错误
下面的示例加载库
library(dplyr)
library(caret)
library(C50)
从库C50加载流失数据集
data(churn)
创建x和y变量
churn_x <- subset(churnTest, select= -churn)
churn_y <- churnTest[[20]]
使用createFolds（）在churn_y（目标变量）上创建5个CV折叠
myFolds <- createFolds(churn_y, k = 5)
创建trainControl对象：myControl
myControl <- trainControl(
summaryFunction = twoClassSummary,
classProbs = TRUE, # IMPORTANT!
verboseIter = TRUE,
savePredictions = TRUE,
index = myFolds
)
适合glmnet模型：model_glmnet
model_glmnet <- train(
x = churn_x, y = churn_y,
metric = "ROC",
method = "glmnet",
trControl = myControl
)
我收到以下错误
lognet（x，is.sparse，ix，jx，y，权重，偏移量，alpha，nobs，错误：：NA / NaN / Inf在外部函数调用中（arg 5）另外：警告消息：在lognet（x，is .sparse，ix，jx，y，权重，偏移量，alpha，nobs：NAS由强制性引入
我已经检查过，并且churn_x变量中没有缺失值
sum(is.na(churn_x))
有人知道答案吗？
我在R中使用插入符号进行逻辑回归:
ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10,
savePredictions = TRUE)
mod_fit <- train(Y ~ ., data=df, method="glm", family="binomial",
trControl = ctrl)
print(mod_fit)
打印的默认指标是准确度和Cohen kappa.我想提取匹配的指标,如敏感性,特异性,阳性预测值等,但我找不到一个简单的方法来做到这一点.提供了最终的模型,但它对所有数据进行了训练(据我从文档中可以看出),所以我不能用它来重新预测.
混淆矩阵计算所有必需参数,但将其作为汇总函数传递不起作用:
ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10,
savePredictions = TRUE, summaryFunction = confusionMatrix)
mod_fit <- train(Y ~ ., data=df, method="glm", family="binomial",
trControl = ctrl)
Error: `data` and `reference` should be factors with the same levels.
13.
stop("`data` and `reference` should be factors with the same levels.", …我正在尝试使用这些包quanteda,caret并根据经过训练的样本对文本进行分类.作为试运行,我想比较的内置的朴素贝叶斯分类器quanteda与的那些caret.但是,我似乎caret无法正常工作.
这是一些复制代码.首先是quanteda侧面:
library(quanteda)
library(quanteda.corpora)
library(caret)
corp <- data_corpus_movies
set.seed(300)
id_train <- sample(docnames(corp), size = 1500, replace = FALSE)
# get training set
training_dfm <- corpus_subset(corp, docnames(corp) %in% id_train) %>%
dfm(stem = TRUE)
# get test set (documents not in id_train, make features equal)
test_dfm <- corpus_subset(corp, !docnames(corp) %in% id_train) %>%
dfm(stem = TRUE) %>%
dfm_select(pattern = training_dfm,
selection = "keep")
# train model on sentiment
nb_quanteda <- textmodel_nb(training_dfm, …