Question

我的代码如下：

library('RMySQL')
library('DMwR')
library('tm')
library('Snowball')
library('SnowballC')

con <- dbConnect(MySQL(), user="root", password="stuff0645", dbname="TwitterCelebs", host="localhost")

rt_outlier <- dbGetQuery(con, "SELECT *,tweet_text from outlier_info,tweets where outlier_info.tweet_id=tweets.tweet_id limit 500")

rt_not_outlier <- dbGetQuery(con,"Select *, tweet_text from not_outlier_info,tweets where not_outlier_info.tweet_id=tweets.tweet_id limit 500");

dbDisconnect(con)

all_tweets = rbind(rt_outlier,rt_not_outlier)
all_tweets[,"tweet_text"] <- iconv(all_tweets[,"tweet_text"], to = "utf-8")

corpus = Corpus(VectorSource(all_tweets[,"tweet_text"]))
corpus = tm_map(corpus,removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, stemDocument)
corpus <- tm_map(corpus,removeWords,stopwords("english"))
corpus <- tm_map(corpus,stripWhitespace)
corpus <- tm_map(corpus,removeNumbers)
mydata.dtm <- TermDocumentMatrix(corpus,control=list(weighting=weightTfIdf, minWordLength=2, findFreqTerms=5))


dataframe <- as.data.frame(inspect(mydata.dtm))

d=as.data.frame(t(dataframe))

classData = c(rep(0,500),rep(1,500))
classData = as.factor(classData)

library('caret')
ctrl = trainControl(method = "repeatedcv", repeats = 3,)
set.seed(2)
mymodel <- train(d, classData,trControl=ctrl,method="J48",model=FALSE)

基本上，这里发生的是我不断收到错误和警告：

Error in train.default(d, classData, method = "J48", model = FALSE) : 
  final tuning parameters could not be determined
In addition: Warning messages:
1: In train.default(d, classData, method = "J48", model = FALSE) :
  Models using Weka will not work with parallel processing with multicore/doMC
2: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,  :
  There were missing values in resampled performance measures.
3: In train.default(d, classData, method = "J48", model = FALSE) :
  missing values found in aggregated results

我做错了什么？另请注意，我在训练中使用model = FALSE来节省内存，因为这是一个问题

Answer 1

您是否看到了消息：

“Models using Weka will not work with parallel processing with multicore/doMC”？

尝试按顺序运行。

更好的是，并行使用C5.0。 J48不是C4.5的绝佳实现，尤其是在分割分类预测变量时。

最高

＆＃34;错误：无法确定最终调整参数＆＃34;当使用method =＆＃34; J48＆＃34;进行分类时

1 个答案: