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Suppose we want to use the caret library to tune a random forest model. We want to repeat 5 times a cross-validation with 8 folds, and use 800 trees. We want to explore the following values of the mtry hyperparameter: 1,3 and 10. Which of the following codes looks like the correct one to run? currentmodel <- train(Creditability-.. data - dtrain, method = 'rf', metric = 'Accuracy'. tuneGrid - expand.grid(.mtry = c(1,3,10)), trControl-trainControl(method='cv', 5 number repeats = 8, ntree = ntree) currentmodel <- train(Creditability. data = dtrain, method = 'rf', metric = "Accuracy'. tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl-trainControl(method = 'repeatedcv', number = 8, repeats = 5, search = 'grid'), ntree = 800) currentmodel <- train(Creditability. data - dtrain, method = 'caret', metric = 'Accuracy', tuneGrid = expand.grid(.mtryc(10,3,10)), trControl - trainControl(method = 'repeatedcv', number - 8, repeats - 5, search = 'random'), ntree - 800)

Suppose we want to use the caret library to tune a random forest model. We want to repeat 5 times a cross-validation with 8 folds, and use 800 trees. We want to explore the following values of the mtry hyperparameter: 1,3 and 10. Which of the following codes looks like the correct one to run? currentmodel <- train(Creditability-.. data - dtrain, method = 'rf', metric = 'Accuracy'. tuneGrid - expand.grid(.mtry = c(1,3,10)), trControl-trainControl(method='cv', 5 number repeats = 8, ntree = ntree) currentmodel <- train(Creditability. data = dtrain, method = 'rf', metric = "Accuracy'. tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl-trainControl(method = 'repeatedcv', number = 8, repeats = 5, search = 'grid'), ntree = 800) currentmodel <- train(Creditability. data - dtrain, method = 'caret', metric = 'Accuracy', tuneGrid = expand.grid(.mtryc(10,3,10)), trControl - trainControl(method = 'repeatedcv', number - 8, repeats - 5, search = 'random'), ntree - 800)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Suppose we want to use the caret library to tune a random forest model. We want to repeat 5 times a cross-validation with 8 folds, and use 800 trees. We want to explore the following values of the mtry hyperparameter: 1,3 and 10. Which of the following codes looks like the correct one to run? currentmodel <- train(Creditability-... data - dtrain, method = 'rf, metric = 'Accuracy'. tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl - trainControl(method='cv', number = 5 repeats = 8, ntree = ntree) currentmodel < - train(Creditability. data-dtrain, method = 'rf', metric'Accuracy', tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl = trainControl(method = 'repeatedcv', number = 8, repeats = 5, search = 'grid'), ntree = 800) currentmodel <-train(Creditability... data-dtrain, method = 'caret', metric = 'Accuracy, tuneGrid = expand.grid(.mtry = c(10,3,10)), trControl - trainControl(method= 'repeatedcv', number = 8, repeats = 5, search 'random'), ntree = 800)
Transcribed Image Text:Suppose we want to use the caret library to tune a random forest model. We want to repeat 5 times a cross-validation with 8 folds, and use 800 trees. We want to explore the following values of the mtry hyperparameter: 1,3 and 10. Which of the following codes looks like the correct one to run? currentmodel <- train(Creditability-... data - dtrain, method = 'rf, metric = 'Accuracy'. tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl - trainControl(method='cv', number = 5 repeats = 8, ntree = ntree) currentmodel < - train(Creditability. data-dtrain, method = 'rf', metric'Accuracy', tuneGrid = expand.grid(.mtry = c(1,3,10)), trControl = trainControl(method = 'repeatedcv', number = 8, repeats = 5, search = 'grid'), ntree = 800) currentmodel <-train(Creditability... data-dtrain, method = 'caret', metric = 'Accuracy, tuneGrid = expand.grid(.mtry = c(10,3,10)), trControl - trainControl(method= 'repeatedcv', number = 8, repeats = 5, search 'random'), ntree = 800)
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