r - 基于算法进行预测时出错

我有一个像这样组织的数据集:

library(lubridate)
library(e1071)
library(rpart)
library(pROC)
library(rpart.plot)
library(RColorBrewer)
library(ada)
library(maboost)
library(adabag)
library(ROCR)
library(data.table)

> head(crypto_data)
                  time btc_price eth_price block_size difficulty estimated_btc_sent estimated_transaction_volume_usd  hash_rate
1: 2017-09-02 21:54:00  4537.834   330.727  142521291   8.88e+11           2.04e+13                        923315360 7417412092
2: 2017-09-02 22:29:00  4577.605   337.804  136524566   8.88e+11           2.03e+13                        918188067 7152504517
3: 2017-09-02 23:04:00  4566.360   336.938  134845546   8.88e+11           2.01e+13                        910440916 7240807042
4: 2017-09-02 23:39:00  4590.031   342.929  133910638   8.88e+11           1.99e+13                        901565930 7284958305
5: 2017-09-03 00:14:00  4676.193   354.171  130678099   8.88e+11           2.01e+13                        922422228 7152504517
6: 2017-09-03 00:49:00  4699.936   352.299  127557140   8.88e+11           1.99e+13                        910457430 7064201992
   miners_revenue_btc miners_revenue_usd minutes_between_blocks n_blocks_mined n_blocks_total n_btc_mined   n_tx nextretarget total_btc_sent
1:               2395           10839520                   8.00            168         483207    2.10e+11 241558       483839       1.62e+14
2:               2317           10482320                   8.33            162         483208    2.03e+11 236661       483839       1.60e+14
3:               2342           10596900                   8.22            164         483216    2.05e+11 238682       483839       1.60e+14
4:               2352           10642439                   8.14            165         483220    2.06e+11 237159       483839       1.58e+14
5:               2316           10611798                   8.38            162         483223    2.03e+11 237464       483839       1.58e+14
6:               2288           10481960                   8.41            160         483226    2.00e+11 234472       483839       1.57e+14
   total_fees_btc totalbtc trade_volume_btc trade_volume_usd targetVar
1:    29597881711 1.65e+15        102451.92        463497285       buy
2:    29202300823 1.65e+15        102451.92        463497285       buy
3:    29234981721 1.65e+15        102451.92        463497285       buy
4:    28991577368 1.65e+15        102451.92        463497285       buy
5:    29179041967 1.65e+15         96216.78        440710136      hold
6:    28844391629 1.65e+15         96216.78        440710136      hold

然后，我创建了一个函数:

classification_error <- function(conf_mat) {
  conf_mat = as.matrix(conf_mat)

  error = 1-sum(diag(conf_mat))/sum(conf_mat)

  return (list(conf_mat = conf_mat,
               error = error))
}
    predFunc <- function(inData,trainPct){



  trainP <- trainPct * .6
  valP <- trainPct * .2
  testP <- trainPct * .2

  #SplitData
  trainObs <- sample(nrow(inData), trainP * nrow(inData), replace = FALSE)
  valObs <- sample(nrow(inData), valP * nrow(inData), replace = FALSE)
  testObs <- sample(nrow(inData), testP * nrow(inData), replace = FALSE)

  # Create the training/va/test datasets
  trainDS <- inData[trainObs,]
  valDS <- inData[valObs,]
  testDS <- inData[testObs,]

  # SVM- linear kernel
  linearSVM <- svm(targetVar ~ ., data = trainDS, method = "C-classification", kernel = "linear")


  # linear SVM Predictions
  predSVMlin <- predict(linearSVM, trainDS[,-c("targetVar")])
  valSVMlin <- predict(linearSVM, valDS[,-c("targetVar")])
  testSVMlin <- predict(linearSVM, testDS[,-c("targetVar")])

  # SVM Confusion matrix
  trainConfusion <- table(true = trainDS[,c("targetVar")], pred = predSVMlin)
  valConfusion <- table(true = trainDS[,c("targetVar")], pred = valSVMlin)
  testConfusion <- table(true = trainDS[,c("targetVar")], pred = testSVMlin)

  # Linear SVM Classification error
  trainClassificationError <- classification_error(trainConfusion)
  valClassificationError <- classification_error(valConfusion)
  testClassificationError <- classification_error(testConfusion)

  return(list(trainError = trainClassificationError,
              valError = valClassificationError,
              testError = testClassificationError,
              ))
}

然后我调用了函数:crypt <- predFunc(crypto_data, .7)，但是当我运行此代码时，出现以下错误:



基本上，我认为该函数在创建混淆矩阵时会遇到问题，但我无法理解原因或解决方法。有什么建议吗？

最佳答案

在执行线性SVM预测时，应使用如下所示的smth:
predSVMlin <- predict(linearSVM, trainDS %>% select(-targetVar))

在# SVM Confusion matrix中，您可以使用trainDS$targetVar生成数字 vector ，而不是dataframe列。

这取决于您计算的指标，但是在我看来最后两个步骤不清楚，您能解释一下吗？

我找不到classification_error函数，请在发现它的地方写或者写源代码。