View source: R/wrapper_feature_selection.R
wrapper_feat_select | R Documentation |
This function is a wrapper for the feature_selection function
wrapper_feat_select(
X,
y,
params_glmnet = NULL,
params_xgboost = NULL,
params_ranger = NULL,
xgb_sort = NULL,
CV_folds = 5,
stratified_regr = FALSE,
scale_coefs_glmnet = FALSE,
cores_glmnet = NULL,
params_features = NULL,
verbose = FALSE
)
X |
a sparse Matrix, a matrix or a data frame |
y |
a vector of length representing the response variable |
params_glmnet |
a list of parameters for the glmnet model |
params_xgboost |
a list of parameters for the xgboost model |
params_ranger |
a list of parameters for the ranger model |
xgb_sort |
sort the xgboost features by "Gain", "Cover" or "Frequency" ( defaults to "Frequency") |
CV_folds |
a number specifying the number of folds for cross validation |
stratified_regr |
a boolean determining if the folds in regression should be stratified |
scale_coefs_glmnet |
if TRUE, less important coefficients will be smaller than the more important ones (ranking/plotting by magnitude possible) |
cores_glmnet |
an integer determining the number of cores to register in glmnet |
params_features |
is a list of parameters for the wrapper function |
verbose |
outputs info |
This function returns the importance of the methods specified and if union in the params_feature list is TRUE then it also returns the average importance of all methods. Furthermore the user can limit the number of features using the keep_number_feat parameter of the params_feature list.
a list containing the important features of each method. If union in the params_feature list is enabled, then it also returns the average importance of all methods.
## Not run:
#...........
# regression
#...........
data(iris)
X = iris[, -5]
y = X[, 1]
X = X[, -1]
params_glmnet = list(alpha = 1,
family = 'gaussian',
nfolds = 3,
parallel = TRUE)
params_xgboost = list( params = list("objective" = "reg:linear",
"bst:eta" = 0.01,
"subsample" = 0.65,
"max_depth" = 5,
"colsample_bytree" = 0.65,
"nthread" = 2),
nrounds = 100,
print.every.n = 50,
verbose = 0,
maximize = FALSE)
params_ranger = list(probability = FALSE,
num.trees = 100,
verbose = TRUE,
classification = FALSE,
mtry = 3,
min.node.size = 10,
num.threads = 2,
importance = 'permutation')
params_features = list(keep_number_feat = NULL,
union = TRUE)
feat = wrapper_feat_select(X,
y,
params_glmnet = params_glmnet,
params_xgboost = params_xgboost,
params_ranger = params_ranger,
xgb_sort = NULL,
CV_folds = 10,
stratified_regr = FALSE,
cores_glmnet = 2,
params_features = params_features)
## End(Not run)
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