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Change SMOTE parameters inside CARET k-fold cross-validation classification


Change SMOTE parameters inside CARET k-fold cross-validation classification

By : Aye Htet Htet Naing
Date : November 22 2020, 02:42 PM
hope this fix your issue I am not sure to have understood what you do not understand but here is an attempt to clarify what is done in this piece of code.
The smotest object is created as list because it is the way the argument sampling of trainControl function must be represented. The first element of this list is a name used only for display purposes. The second, func, is the actual sampling function. The third, first, is a logical value indicating whether samplin must be done before or after the pre-processing step.
code :


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Repeated balanced k-fold cross validation using caret in R

Repeated balanced k-fold cross validation using caret in R


By : apfrogerio
Date : March 29 2020, 07:55 AM
will help you The answer is yes.
If the method = "repeatedcv" it calls on the function createMultiFolds, which internally calls on createFolds, but n times as specified in repeats = n
Test set and train set for each fold in Caret cross validation

Test set and train set for each fold in Caret cross validation


By : Ashwini Zinavo
Date : March 29 2020, 07:55 AM
Hope that helps I tried to understand the 5 fold cross validation algorithm in Caret package but I could not find out how to get train set and test set for each fold and I also could not find this from the similar suggested questions. Imagine if I want to do cross validation by random forest method, I do the following:
code :
 str(rfmodel)
 names(rfmodel)
 #  [1] "method"       "modelInfo"    "modelType"    "results"      "pred"        
 #  [6] "bestTune"     "call"         "dots"         "metric"       "control"     
 # [11] "finalModel"   "preProcess"   "trainingData" "resample"     "resampledCM" 
 # [16] "perfNames"    "maximize"     "yLimits"      "times"        "levels"      
 # [21] "terms"        "coefnames"    "xlevels" 
 # Indexes of Hold Out Sets
 rfmodel$control$indexOut

 # Indexes of Train Sets for above hold outs
 rfmodel$control$index
How does Caret generate an OLS model with K-fold cross validation?

How does Caret generate an OLS model with K-fold cross validation?


By : Steven K
Date : March 29 2020, 07:55 AM
this will help Let's say I have some generic dataset for which an OLS regression is the best choice. So, I generate a model with some first-order terms and decide to use Caret in R for my regression coefficient estimates and error estimates. , Here's reproducible data for your example.
code :
library("caret")
my_data <- iris

k10_cv <- trainControl(method="cv", number=10)

set.seed(100)
ols_model <- train(Sepal.Length ~  Sepal.Width + Petal.Length + Petal.Width,
                  data = my_data, trControl = k10_cv, method = "lm")


> ols_model$results
  intercept      RMSE  Rsquared       MAE     RMSESD RsquaredSD      MAESD
1      TRUE 0.3173942 0.8610242 0.2582343 0.03881222 0.04784331 0.02960042
> (ols_model$resample)
        RMSE  Rsquared       MAE Resample
1  0.3386472 0.8954600 0.2503482   Fold01
2  0.3154519 0.8831588 0.2815940   Fold02
3  0.3167943 0.8904550 0.2441537   Fold03
4  0.2644717 0.9085548 0.2145686   Fold04
5  0.3769947 0.8269794 0.3070733   Fold05
6  0.3720051 0.7792611 0.2746565   Fold06
7  0.3258501 0.8095141 0.2647466   Fold07
8  0.2962375 0.8530810 0.2731445   Fold08
9  0.3059100 0.8351535 0.2611982   Fold09
10 0.2615792 0.9286246 0.2108592   Fold10
> mean(ols_model$resample$RMSE)==ols_model$results$RMSE
[1] TRUE
 coef(lm(Sepal.Length ~  Sepal.Width + Petal.Length + Petal.Width, data = my_data))
 (Intercept)  Sepal.Width Petal.Length  Petal.Width 
   1.8559975    0.6508372    0.7091320   -0.5564827 
How to get predictions for each fold in 10-fold cross-validation of the best tuned hyperparameters using caret package i

How to get predictions for each fold in 10-fold cross-validation of the best tuned hyperparameters using caret package i


By : user2961092
Date : March 29 2020, 07:55 AM
may help you . One way to achieve your goal is to subset fit.svm$pred using the hyper parameters in fit.svm$bestTune, and then aggregate the desired measure by CV replicates. I will perform this using dplyr:
code :
library(tidyverse)
library(caret)
fit.svm$pred %>%
  filter(sigma == fit.svm$bestTune$sigma & C == fit.svm$bestTune$C) %>% #subset 
  mutate(fold = gsub("\\..*", "", Resample), #extract fold info from resample info
         rep = gsub(".*\\.(.*)", "\\1", Resample)) %>% #extract replicate info from resample info
  group_by(rep) %>% #group by replicate
  summarise(rmse = RMSE(pred, obs)) #aggregate the desired measure
# A tibble: 3 x 2
  rep    rmse
  <chr> <dbl>
1 Rep1   4.02
2 Rep2   3.96
3 Rep3   4.06
fit.svm$pred %>%
  filter(sigma == fit.svm$bestTune$sigma & C == fit.svm$bestTune$C) %>%
  separate(Resample, c("fold", "rep"), "\\.") %>%
  group_by(rep) %>%
  summarise(rmse = RMSE(obs, pred))
fit.svm$pred %>%
  filter(sigma == fit.svm$bestTune$sigma & C == fit.svm$bestTune$C) %>%
  write.csv("predictions.csv")
Regarding the Caret package in R when apply K fold cross validation

Regarding the Caret package in R when apply K fold cross validation


By : user3077919
Date : March 29 2020, 07:55 AM
Hope that helps It can be done by cross tabulating the observed and predicted values as follows,
code :
table((mod_fit$pred)$obs,(mod_fit$pred)$pred)
      Down  Up
  Down  125 477
  Up    151 497
overall missclassification = (125+497)/250 = 0.4976

sensitivity =  497/(151+497) = 0.7770 
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