R, caret: does caret automatically create dummy variable for random Forest?
$begingroup$
I created a two class data set, dat. This data set only contains 25 variables. 24 are numeric and 1 is categorical (with 26 levels). The result of the auto-tuned model gives a mtry of 2, 14, 26, 38 and 50. So is this true that the caret created the dummy variable for the categorical variable? What other models does caret create dummy variable automatically?
Please see following example code.
pacman::p_load(R.utils, caret, doParallel)
cl <- makeCluster(8, outfile="")
registerDoParallel(cl)
set.seed(2)
dat <- twoClassSim(48842, noiseVars = 10)# 25 variables.
dat = cbind(dat,data.frame(categorical = sample(LETTERS,nrow(dat),replace = T)))
fit_on <- list(rs1 = 1:1300)
pred_on <- list(rs1 = 1330:10000)
indexFinal = 1:1000
fitControl <- trainControl(method = "cv",number=1,repeats=1,
classProbs = TRUE,summaryFunction = twoClassSummary,
index= fit_on, indexOut = pred_on,
indexFinal = indexFinal,
verboseIter = TRUE,
savePredictions = TRUE
)
model = train(Class ~ ., data = dat,
method = "rf",
preProcess=c("center","scale"),
trControl = fitControl,
tuneLength = 5)
model
# mtry ROC Sens Spec
# 2 0.9166711 0.9298806 0.6778085
# 14 0.9237881 0.8851236 0.7645137
# 26 0.9177710 0.8746803 0.7770797
# 38 0.9132661 0.8689258 0.7753204
# 50 0.9093577 0.8687127 0.7728072
# there are only 25 variables.
stopCluster(cl)
machine-learning r
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
$endgroup$
add a comment |
$begingroup$
I created a two class data set, dat. This data set only contains 25 variables. 24 are numeric and 1 is categorical (with 26 levels). The result of the auto-tuned model gives a mtry of 2, 14, 26, 38 and 50. So is this true that the caret created the dummy variable for the categorical variable? What other models does caret create dummy variable automatically?
Please see following example code.
pacman::p_load(R.utils, caret, doParallel)
cl <- makeCluster(8, outfile="")
registerDoParallel(cl)
set.seed(2)
dat <- twoClassSim(48842, noiseVars = 10)# 25 variables.
dat = cbind(dat,data.frame(categorical = sample(LETTERS,nrow(dat),replace = T)))
fit_on <- list(rs1 = 1:1300)
pred_on <- list(rs1 = 1330:10000)
indexFinal = 1:1000
fitControl <- trainControl(method = "cv",number=1,repeats=1,
classProbs = TRUE,summaryFunction = twoClassSummary,
index= fit_on, indexOut = pred_on,
indexFinal = indexFinal,
verboseIter = TRUE,
savePredictions = TRUE
)
model = train(Class ~ ., data = dat,
method = "rf",
preProcess=c("center","scale"),
trControl = fitControl,
tuneLength = 5)
model
# mtry ROC Sens Spec
# 2 0.9166711 0.9298806 0.6778085
# 14 0.9237881 0.8851236 0.7645137
# 26 0.9177710 0.8746803 0.7770797
# 38 0.9132661 0.8689258 0.7753204
# 50 0.9093577 0.8687127 0.7728072
# there are only 25 variables.
stopCluster(cl)
machine-learning r
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
$endgroup$
add a comment |
$begingroup$
I created a two class data set, dat. This data set only contains 25 variables. 24 are numeric and 1 is categorical (with 26 levels). The result of the auto-tuned model gives a mtry of 2, 14, 26, 38 and 50. So is this true that the caret created the dummy variable for the categorical variable? What other models does caret create dummy variable automatically?
Please see following example code.
pacman::p_load(R.utils, caret, doParallel)
cl <- makeCluster(8, outfile="")
registerDoParallel(cl)
set.seed(2)
dat <- twoClassSim(48842, noiseVars = 10)# 25 variables.
dat = cbind(dat,data.frame(categorical = sample(LETTERS,nrow(dat),replace = T)))
fit_on <- list(rs1 = 1:1300)
pred_on <- list(rs1 = 1330:10000)
indexFinal = 1:1000
fitControl <- trainControl(method = "cv",number=1,repeats=1,
classProbs = TRUE,summaryFunction = twoClassSummary,
index= fit_on, indexOut = pred_on,
indexFinal = indexFinal,
verboseIter = TRUE,
savePredictions = TRUE
)
model = train(Class ~ ., data = dat,
method = "rf",
preProcess=c("center","scale"),
trControl = fitControl,
tuneLength = 5)
model
# mtry ROC Sens Spec
# 2 0.9166711 0.9298806 0.6778085
# 14 0.9237881 0.8851236 0.7645137
# 26 0.9177710 0.8746803 0.7770797
# 38 0.9132661 0.8689258 0.7753204
# 50 0.9093577 0.8687127 0.7728072
# there are only 25 variables.
stopCluster(cl)
machine-learning r
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
$endgroup$
I created a two class data set, dat. This data set only contains 25 variables. 24 are numeric and 1 is categorical (with 26 levels). The result of the auto-tuned model gives a mtry of 2, 14, 26, 38 and 50. So is this true that the caret created the dummy variable for the categorical variable? What other models does caret create dummy variable automatically?
Please see following example code.
pacman::p_load(R.utils, caret, doParallel)
cl <- makeCluster(8, outfile="")
registerDoParallel(cl)
set.seed(2)
dat <- twoClassSim(48842, noiseVars = 10)# 25 variables.
dat = cbind(dat,data.frame(categorical = sample(LETTERS,nrow(dat),replace = T)))
fit_on <- list(rs1 = 1:1300)
pred_on <- list(rs1 = 1330:10000)
indexFinal = 1:1000
fitControl <- trainControl(method = "cv",number=1,repeats=1,
classProbs = TRUE,summaryFunction = twoClassSummary,
index= fit_on, indexOut = pred_on,
indexFinal = indexFinal,
verboseIter = TRUE,
savePredictions = TRUE
)
model = train(Class ~ ., data = dat,
method = "rf",
preProcess=c("center","scale"),
trControl = fitControl,
tuneLength = 5)
model
# mtry ROC Sens Spec
# 2 0.9166711 0.9298806 0.6778085
# 14 0.9237881 0.8851236 0.7645137
# 26 0.9177710 0.8746803 0.7770797
# 38 0.9132661 0.8689258 0.7753204
# 50 0.9093577 0.8687127 0.7728072
# there are only 25 variables.
stopCluster(cl)
machine-learning r
machine-learning r
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
asked Mar 17 '17 at 23:42
WCMCWCMC
23326
23326
add a comment |
add a comment |
1 Answer
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Yes, it does for some models. You can inspect which variables are used via
model$coefnames.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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1 Answer
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active
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active
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$begingroup$
Yes, it does for some models. You can inspect which variables are used via
model$coefnames.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Yes, it does for some models. You can inspect which variables are used via
model$coefnames.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Yes, it does for some models. You can inspect which variables are used via
model$coefnames.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Yes, it does for some models. You can inspect which variables are used via
model$coefnames.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
answered 12 mins ago
Marcus LauritsenMarcus Lauritsen
1
1
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Marcus Lauritsen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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