title: How to use the 'adaboost' method to Build Classification Trees wthin the Caret and fastAdaboost Packages in R
body: <p><strong>Issue</strong></p>
  <p>I'm attempting to use the <code>'adaboost'</code> method within the <code>Caret</code> and <code>fastAdaboost</code> packages. My objective is to build a <code>classification tree</code> using `machine learning techniques in R for an upcoming project at university and I am following this tutorial <a href="https://www.machinelearningplus.com/machine-learning/caret-package/" rel="nofollow noreferrer">here</a>.</p>
  <p>For this model <strong>(see below)</strong>, I have downloaded the libraries <code>caret</code> and <code>fastAdaboost</code> and whenever I try to run my model, I get the message.</p>
  <pre><code>Error: object 'model_adaboost' not found
  </code></pre>
  <p>I don't understand what's wrong with this code <strong>(see below)</strong> as it's identical to my other models and I don't know why R cannot find my model.</p>
  <p>Many thanks if anyone can lend a hand.</p>
  <p><strong>These models are running just fine:</strong></p>
  <pre><code>#Random Forest
  **# Train the model using rf
  model_rf = train(Country ~., data=train.data, method='rf', metric=metric, tuneLength= tuneLength, trControl = fitControl)
  
  model_rf
  
  #Naive Bayes
  nb_tune &lt;- data.frame(usekernel = TRUE, fL = 0, adjust=seq(0, 5, by = 1))
  
  model.nb1 = train(Country ~., data=train.data,'nb', trControl=fitControl, metric=metric, tuneLength=tuneLength, tuneGrid = nb_tune, laplace = 0:3)
  model.nb1
  </code></pre>
  <p><strong>Structure of my data frame</strong></p>
  <pre><code>data.frame':    367 obs. of  10 variables:
   $ Country    : Factor w/ 3 levels &quot;Italy&quot;,&quot;Turkey&quot;,..: 2 3 1 3 2 3 3 2 3 3 ...
   $ Low.Freq   : num  -0.1 0.381 0.705 0.441 -0.603 ...
   $ High.Freq  : num  -0.503 0.96 -0.371 0.207 -0.336 ...
   $ Peak.Freq  : num  -0.4751 -0.0966 -0.2089 -0.1952 -0.3184 ...
   $ Delta.Freq : num  -0.334 0.122 -0.567 -0.148 -0.132 ...
   $ Delta.Time : num  -0.445 1.565 -1.145 0.131 0.666 ...
   $ Peak.Time  : num  0.0289 0.1897 -0.4765 -0.029 0.1492 ...
   $ Center.Freq: num  -0.5294 -0.0507 -0.1589 -0.0819 -0.405 ...
   $ Start.Freq : num  0.672 1.787 0.403 0.388 -1.068 ...
   $ End.Freq   : num  -0.5393 -0.8247 -0.0148 -0.9138 0.0482 ...
  </code></pre>
  <p><strong>R CODE</strong></p>
  <pre><code>library(caret)
  library(fastAdaboost)
  
  #Data is 'Clusters_Dummy_2'
  
  ##Produce a new version of the dataframe 'Clusters_Dummy' with the rows shuffled
  NewClusters=Cluster_Dummy_2[sample(1:nrow(Cluster_Dummy_2)),]
  
  #Produce a dataframe
  NewCluster&lt;-as.data.frame(NewClusters)
  
  #display
  print(NewCluster)
  
  #Check the structure of the data
  str(NewCluster)
  
  #Number of rows
  nrow(NewCluster)
  
  #Split the data frame into 70% to 30% train and test data
  training.parameters &lt;- Cluster_Dummy_2$Country %&gt;% 
  createDataPartition(p = 0.7, list = FALSE)
  train.data &lt;- NewClusters[training.parameters, ]
  test.data &lt;- NewClusters[-training.parameters, ]
  
  
  ##Auxiliary function for controlling model fitting      
  
  fitControl &lt;- trainControl(## 10-fold CV
                            method = &quot;repeatedcv&quot;,
                            number = 10,
                            ## repeated ten times
                            repeats = 10,
                            classProbs = TRUE,
                            verbose = TRUE)
  
  
  fitGrid_2 &lt;- expand.grid(mfinal = (1:3)*3,         # This is new!
                           maxdepth = c(1, 3),       # ...and this
                           coeflearn = c(&quot;Breiman&quot;),
                           iter=100) # ...and this
  
  model_adaboost = train(Country ~ ., data=train.data, method='adaboost', tuneLength = tuneLength, metric=metric, trControl = fitControl,
                         tuneGrid=fitGrid_2, verbose=TRUE)
  model_adaboost
  </code></pre>
  <p><strong>Data</strong></p>
  <pre><code> structure(list(Low.Freq = c(435L, 94103292L, 1L, 2688L, 8471L, 
      28818L, 654755585L, 468628164L, 342491L, 2288474L, 3915L, 411L, 
      267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L, 9544861L, 
      3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L, 2445L, 
      8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 605L, 
      9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L, 28934316L, 
      7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L, 6L, 90975L, 
      83103577L, 9529L, 229093L, 42810L, 5L, 18175302L, 1443751L, 5831L, 
      8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L, 7434328L, 
      82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L, 0L, 936779338L, 
      4885076L, 745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 
      4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L, 5690L, 56L, 
      3561L, 78738L, 1803363L, 809369L, 7131L, 0L), High.Freq = c(6071L, 
      3210L, 6L, 7306092L, 6919054L, 666399L, 78L, 523880161L, 4700783L, 
      4173830L, 30L, 811L, 341014L, 780L, 44749L, 91L, 201620707L, 
      74L, 1L, 65422L, 595L, 89093186L, 946520L, 6940919L, 655350L, 
      4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L, 90519642L, 984L, 
      0L, 296209525L, 487088392L, 5L, 894L, 529L, 5L, 99106L, 2L, 926017L, 
      9078L, 1L, 21L, 88601017L, 575770L, 48L, 8431L, 194L, 62324996L, 
      5L, 81L, 40634727L, 806901520L, 6818173L, 3501L, 91780L, 36106039L, 
      5834347L, 58388837L, 34L, 3280L, 6507606L, 19L, 402L, 584L, 76L, 
      4078684L, 199L, 6881L, 92251L, 81715L, 40L, 327L, 57764L, 97668898L, 
      2676483L, 76L, 4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L, 
      9724L, 21L, 4L, 359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L, 
      35544L), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L, 
      32840L, 62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L, 
      85315406L, 703037627L, 331264L, 8403609L, 3934064L, 50578953L, 
      370110665L, 3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L, 
      69467L, 75L, 500297L, 704L, 1L, 102659072L, 60896923L, 4481230L, 
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      3072127L, 2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L, 
      522433683L, 112844L, 193385L, 458269L, 93578705L, 22093131L, 
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      97809006L, 90L, 6L, 260792844L, 9L, 833205723L, 99467321L, 5L, 
      8455640L, 54090L, 2L, 309L, 299161148L, 4952L, 454824L), Delta.Freq = c(5L, 
      78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L, 
      817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L, 
      6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L, 
      599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L, 
      4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L, 
      73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L, 
      3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L, 
      1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L, 
      4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L, 
      1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L, 
      47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L, 
      72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L), Delta.Time = c(1361082L, 
      7926L, 499L, 5004L, 3494530L, 213L, 64551179L, 70L, 797L, 5L, 
      72588L, 86976L, 5163L, 635080L, 3L, 91L, 919806257L, 81443L, 
      3135427L, 4410972L, 5810L, 8L, 46603718L, 422L, 1083626L, 48L, 
      15699890L, 7L, 90167635L, 446459879L, 2332071L, 761660L, 49218442L, 
      381L, 46L, 493197L, 46L, 798597155L, 45342274L, 6265842L, 6L, 
      3445819L, 351L, 1761227L, 214L, 959L, 908996387L, 6L, 3855L, 
      9096604L, 152664L, 7970052L, 32366926L, 31L, 5201618L, 114L, 
      7806411L, 70L, 239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L, 
      495604L, 29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L, 
      85294L, 580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L, 
      7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L, 890460L, 
      160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L), Peak.Time = c(1465265L, 
      452894L, 545076172L, 8226275L, 5040875L, 700530L, 1L, 3639L, 
      20141L, 71712131L, 686L, 923L, 770569738L, 69961L, 737458636L, 
      122403L, 199502046L, 6108L, 907L, 108078263L, 7817L, 4L, 6L, 
      69L, 721L, 786353L, 87486L, 1563L, 876L, 47599535L, 79295722L, 
      53L, 7378L, 591L, 6607935L, 954L, 6295L, 75514344L, 5742050L, 
      25647276L, 449L, 328566184L, 4L, 2L, 2703L, 21367543L, 63429043L, 
      708L, 782L, 909820L, 478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L, 
      96L, 6L, 716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L, 
      7L, 609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L, 
      84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L, 
      3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L, 
      18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L, 
      417344L, 813L, 55792L, 78L), Center_Freq = c(61907L, 8709547L, 
      300750537L, 45862L, 91417085L, 79892L, 47765L, 5477L, 18L, 4186L, 
      2860L, 754038591L, 375L, 53809223L, 72L, 136L, 4700783L, 4173830L, 
      30L, 811L, 341014L, 780L, 44749L, 91L, 201620707L, 74L, 1L, 65422L, 
      595L, 89093186L, 946520L, 6940919L, 48744L, 2317845L, 5126197L, 
      2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L, 
      9L, 651547554L, 45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 
      3001L, 9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L, 
      555498297L, 60431L, 6597L, 856943893L, 607815536L, 4406L, 79L, 
      4885076L, 745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 
      4440739L, 754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L, 
      13128104L, 1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L, 
      5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L, 21387L
      ), Start.Freq = c(426355L, 22073538L, 680374L, 41771L, 54L, 6762844L, 
      599171L, 108L, 257451851L, 438814L, 343045L, 4702L, 967787L, 
      1937L, 18L, 89301735L, 366L, 90L, 954L, 7337732L, 70891703L, 
      4139L, 10397931L, 940000382L, 7L, 38376L, 878528819L, 6287L, 
      738366L, 31L, 47L, 5L, 6L, 77848L, 2366508L, 45L, 3665842L, 7252260L, 
      6L, 61L, 3247L, 448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L, 
      844927639L, 78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L, 
      1651L, 73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L, 
      7556L, 65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L, 
      280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L, 29L, 
      76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L, 44L, 
      24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L, 65007421L
      ), End.Freq = c(71000996L, 11613579L, 71377155L, 1942738L, 8760748L, 
      79L, 455L, 374L, 8L, 5L, 2266932L, 597833L, 155488L, 3020L, 4L, 
      554L, 4L, 16472L, 1945649L, 668181101L, 649780L, 22394365L, 93060602L, 
      172146L, 20472L, 23558847L, 190513L, 22759044L, 44L, 78450L, 
      205621181L, 218L, 69916344L, 23884L, 66L, 312148L, 7710564L, 
      4L, 422L, 744572L, 651547554L, 45554L, 38493L, 91055218L, 38L, 
      1116474L, 2295482L, 3001L, 9L, 3270L, 141L, 55595L, 38451L, 8660867L, 
      14L, 96L, 345L, 6L, 44L, 8235824L, 910517L, 1424326L, 87102566L, 
      53644L, 667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L, 
      856943893L, 607815536L, 4406L, 79L, 7L, 28978746L, 7537295L, 
      6L, 633L, 345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L, 
      429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L, 
      123367978L, 818775L, 123745614L, 25345654L, 3L), Country = c(&quot;Holland&quot;, 
      &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, 
      &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, 
      &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, 
      &quot;France&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;Holland&quot;, 
      &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, 
      &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;Holland&quot;, &quot;France&quot;, &quot;France&quot;, 
      &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, &quot;Spain&quot;, 
      &quot;Spain&quot;, &quot;Spain&quot;, &quot;France&quot;, &quot;France&quot;, &quot;France&quot;)), row.names = c(NA, 
      99L), class = &quot;data.frame&quot;)
      
  
    [1]: https://www.machinelearningplus.com/machine-learning/caret-package/https://
  </code></pre>