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[R] Prefer descriptive parameter names (#11102)
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‎R-package/R/callbacks.R‎

Lines changed: 20 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -960,17 +960,17 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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#' booster = "gblinear",
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#' objective = "reg:logistic",
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#' eval_metric = "auc",
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#' lambda = 0.0003,
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#' alpha = 0.0003,
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#' reg_lambda = 0.0003,
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#' reg_alpha = 0.0003,
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#' nthread = nthread
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#' )
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#'
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#' # For 'shotgun', which is a default linear updater, using high eta values may result in
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#' # For 'shotgun', which is a default linear updater, using high learning_rate values may result in
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#' # unstable behaviour in some datasets. With this simple dataset, however, the high learning
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#' # rate does not break the convergence, but allows us to illustrate the typical pattern of
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#' # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
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#' bst <- xgb.train(
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#' c(param, list(eta = 1.)),
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#' c(param, list(learning_rate = 1.)),
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#' dtrain,
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#' evals = list(tr = dtrain),
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#' nrounds = 200,
@@ -987,7 +987,7 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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#' c(
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#' param,
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#' xgb.params(
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#' eta = 0.8,
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#' learning_rate = 0.8,
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#' updater = "coord_descent",
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#' feature_selector = "thrifty",
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#' top_k = 1
@@ -1000,12 +1000,20 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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#' )
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#' matplot(xgb.gblinear.history(bst), type = "l")
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#' # Componentwise boosting is known to have similar effect to Lasso regularization.
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#' # Try experimenting with various values of top_k, eta, nrounds,
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#' # Try experimenting with various values of top_k, learning_rate, nrounds,
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#' # as well as different feature_selectors.
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#'
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#' # For xgb.cv:
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#' bst <- xgb.cv(
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#' c(param, list(eta = 0.8)),
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#' c(
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#' param,
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#' xgb.params(
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#' learning_rate = 0.8,
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#' updater = "coord_descent",
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#' feature_selector = "thrifty",
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#' top_k = 1
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#' )
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#' ),
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#' dtrain,
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#' nfold = 5,
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#' nrounds = 100,
@@ -1022,15 +1030,15 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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#' booster = "gblinear",
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#' objective = "multi:softprob",
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#' num_class = 3,
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#' lambda = 0.0003,
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#' alpha = 0.0003,
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#' reg_lambda = 0.0003,
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#' reg_alpha = 0.0003,
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#' nthread = nthread
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#' )
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#'
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#' # For the default linear updater 'shotgun' it sometimes is helpful
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#' # to use smaller eta to reduce instability
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#' # to use smaller learning_rate to reduce instability
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#' bst <- xgb.train(
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#' c(param, list(eta = 0.5)),
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#' c(param, list(learning_rate = 0.5)),
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#' dtrain,
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#' evals = list(tr = dtrain),
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#' nrounds = 50,
@@ -1044,7 +1052,7 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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#'
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#' # CV:
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#' bst <- xgb.cv(
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#' c(param, list(eta = 0.5)),
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#' c(param, list(learning_rate = 0.5)),
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#' dtrain,
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#' nfold = 5,
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#' nrounds = 70,

‎R-package/R/utils.R‎

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -493,7 +493,6 @@ NULL
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.Booster.R‎

Lines changed: 2 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -267,7 +267,6 @@ xgb.get.handle <- function(object) {
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#' nrounds = 5,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 0.5,
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#' nthread = nthread,
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#' objective = "binary:logistic"
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#' )
@@ -312,7 +311,6 @@ xgb.get.handle <- function(object) {
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#' nrounds = 10,
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#' params = xgb.params(
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#' max_depth = 4,
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#' eta = 0.5,
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#' nthread = 2,
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#' subsample = 0.5,
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#' objective = "multi:softprob",
@@ -336,7 +334,6 @@ xgb.get.handle <- function(object) {
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#' nrounds = 10,
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#' params = xgb.params(
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#' max_depth = 4,
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#' eta = 0.5,
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#' nthread = 2,
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#' subsample = 0.5,
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#' objective = "multi:softmax",
@@ -671,7 +668,6 @@ validate.features <- function(bst, newdata) {
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )
@@ -779,7 +775,6 @@ xgb.attributes <- function(object) {
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = nthread,
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#' objective = "binary:logistic"
785780
#' )
@@ -834,13 +829,13 @@ xgb.config <- function(object) {
834829
#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' learning_rate = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
840835
#' )
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#' )
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#'
843-
#' xgb.model.parameters(bst) <- list(eta = 0.1)
838+
#' xgb.model.parameters(bst) <- list(learning_rate = 0.1)
844839
#'
845840
#' @rdname xgb.model.parameters
846841
#' @export
@@ -1285,7 +1280,6 @@ xgb.is.same.Booster <- function(obj1, obj2) {
12851280
#' nrounds = 2,
12861281
#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.create.features.R‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -52,7 +52,7 @@
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#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
5353
#' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
5454
#'
55-
#' param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic', nthread = 1)
55+
#' param <- list(max_depth = 2, learning_rate = 1, objective = 'binary:logistic', nthread = 1)
5656
#' nrounds = 4
5757
#'
5858
#' bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds)

‎R-package/R/xgb.cv.R‎

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Original file line numberDiff line numberDiff line change
@@ -96,7 +96,6 @@
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#' params = xgb.params(
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#' nthread = 2,
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#' max_depth = 3,
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#' eta = 1,
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#' objective = "binary:logistic"
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#' ),
102101
#' nfold = 5,
@@ -316,7 +315,6 @@ xgb.cv <- function(params = xgb.params(), data, nrounds, nfold,
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.dump.R‎

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@@ -34,7 +34,6 @@
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.importance.R‎

Lines changed: 2 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -47,7 +47,6 @@
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = 2,
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#' objective = "binary:logistic"
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#' )
@@ -61,7 +60,7 @@
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#' nrounds = 20,
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#' params = xgb.params(
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#' booster = "gblinear",
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#' eta = 0.3,
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#' learning_rate = 0.3,
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#' nthread = 1,
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#' objective = "binary:logistic"
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#' )
@@ -80,7 +79,6 @@
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#' nrounds = nrounds,
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#' params = xgb.params(
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#' max_depth = 3,
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#' eta = 0.2,
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#' nthread = 2,
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#' objective = "multi:softprob",
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#' num_class = nclass
@@ -110,7 +108,7 @@
110108
#' nrounds = 15,
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#' params = xgb.params(
112110
#' booster = "gblinear",
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#' eta = 0.2,
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#' learning_rate = 0.2,
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#' nthread = 1,
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#' objective = "multi:softprob",
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#' num_class = nclass

‎R-package/R/xgb.load.R‎

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@@ -35,7 +35,6 @@
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = nthread,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.model.dt.tree.R‎

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Original file line numberDiff line numberDiff line change
@@ -47,7 +47,6 @@
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 2,
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#' eta = 1,
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#' nthread = nthread,
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#' objective = "binary:logistic"
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#' )

‎R-package/R/xgb.plot.importance.R‎

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@@ -55,7 +55,6 @@
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#' nrounds = 2,
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#' params = xgb.params(
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#' max_depth = 3,
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#' eta = 1,
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#' nthread = nthread,
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#' objective = "binary:logistic"
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#' )

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