Matt - 1 year ago 161

R Question

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

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

From `?varImp`

:

For models that do not have corresponding

`varImp`

methods, see`filterVarImp`

.

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.

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**