Reputation: 63
I am comparing different machine learning methods using caret, but though the methods are very different, I am getting identical variable contributions.
vNNet, ctree, enet, knn, M5, pcr, ridge, svmRadial give the same variable contributions.
Some of these will take importance = TRUE as input: vNNet, enet, knn, pcr, ridge, svmRadial do. Others generated an error with importance = TRUE: ctree, M5. (The error is "Something is wrong; all the RMSE metric values are missing:")
My question is why do different methods give the same variable importance? This seems wrong, but I can't see what I've done wrong.
library(ggplot2)
library(caret)
library(elasticnet)
library(party)
data_set <- diamonds[1:1000, c(1, 5, 6, 7, 8, 9, 10)]
formula <- price ~ carat + depth + table + x + y + z
set.seed(100)
enet_model <- train(formula,
importance = TRUE,
data = data_set,
method = "enet",
trControl = trainControl(method = "cv"),
preProc = c("center", "scale"))
set.seed(100)
ctree_model <- train(formula,
data = data_set,
method = "ctree",
trControl = trainControl(method = "cv"))
set.seed(Set_seed_seed)
knn_model <- train(formula,
importance = TRUE,
data = data_set,
method = "knn",
preProc = c("center", "scale"),
tuneGrid = data.frame(k = 1:20),
trControl = training_control)
varImp(enet_model)
varImp(ctree_model)
varImp(knn_model)
I'm using caret 6.0-52
Upvotes: 0
Views: 812
Reputation: 14316
From ?varImp
:
For models that do not have corresponding
varImp
methods, seefilterVarImp
.
Those methods don't have importance scores implemented so you get model-free measures. I can add one for enet
based on the coefficient values but knn
and ctree
have no obvious methods.
Upvotes: 1