Deprecate reg:linear' in favor of reg:squarederror'. (#4267)

* Deprecate `reg:linear' in favor of `reg:squarederror'.
* Replace the use of `reg:linear'.
* Replace the use of `silent`.

R-package/R/utils.R

@@ -28,12 +28,12 @@ NVL <- function(x, val) {
 # Merges booster params with whatever is provided in ...
 # plus runs some checks
 check.booster.params <- function(params, ...) {
-  if (typeof(params) != "list") 
+  if (typeof(params) != "list")
     stop("params must be a list")
-  
+
   # in R interface, allow for '.' instead of '_' in parameter names
   names(params) <- gsub("\\.", "_", names(params))
-  
+
   # merge parameters from the params and the dots-expansion
   dot_params <- list(...)
   names(dot_params) <- gsub("\\.", "_", names(dot_params))
@@ -41,15 +41,15 @@ check.booster.params <- function(params, ...) {
                        names(dot_params))) > 0)
     stop("Same parameters in 'params' and in the call are not allowed. Please check your 'params' list.")
   params <- c(params, dot_params)
-  
+
   # providing a parameter multiple times makes sense only for 'eval_metric'
   name_freqs <- table(names(params))
   multi_names <- setdiff(names(name_freqs[name_freqs > 1]), 'eval_metric')
   if (length(multi_names) > 0) {
     warning("The following parameters were provided multiple times:\n\t",
             paste(multi_names, collapse = ', '), "\n  Only the last value for each of them will be used.\n")
-    # While xgboost internals would choose the last value for a multiple-times parameter, 
+    # While xgboost internals would choose the last value for a multiple-times parameter,
-    # enforce it here in R as well (b/c multi-parameters might be used further in R code, 
+    # enforce it here in R as well (b/c multi-parameters might be used further in R code,
     # and R takes the 1st value when multiple elements with the same name are present in a list).
     for (n in multi_names) {
       del_idx <- which(n == names(params))
@@ -57,25 +57,25 @@ check.booster.params <- function(params, ...) {
       params[[del_idx]] <- NULL
     }
   }
-  
+
   # for multiclass, expect num_class to be set
   if (typeof(params[['objective']]) == "character" &&
       substr(NVL(params[['objective']], 'x'), 1, 6) == 'multi:' &&
       as.numeric(NVL(params[['num_class']], 0)) < 2) {
         stop("'num_class' > 1 parameter must be set for multiclass classification")
   }
-  
+
   # monotone_constraints parser
-  
+
   if (!is.null(params[['monotone_constraints']]) &&
       typeof(params[['monotone_constraints']]) != "character") {
         vec2str = paste(params[['monotone_constraints']], collapse = ',')
         vec2str = paste0('(', vec2str, ')')
         params[['monotone_constraints']] = vec2str
   }
-  
+
   # interaction constraints parser (convert from list of column indices to string)
-  if (!is.null(params[['interaction_constraints']]) && 
+  if (!is.null(params[['interaction_constraints']]) &&
       typeof(params[['interaction_constraints']]) != "character"){
     # check input class
     if (class(params[['interaction_constraints']]) != 'list') stop('interaction_constraints should be class list')
@@ -96,10 +96,10 @@ check.booster.params <- function(params, ...) {
 check.custom.obj <- function(env = parent.frame()) {
   if (!is.null(env$params[['objective']]) && !is.null(env$obj))
     stop("Setting objectives in 'params' and 'obj' at the same time is not allowed")
-  
+
   if (!is.null(env$obj) && typeof(env$obj) != 'closure')
     stop("'obj' must be a function")
-  
+
   # handle the case when custom objective function was provided through params
   if (!is.null(env$params[['objective']]) &&
       typeof(env$params$objective) == 'closure') {
@@ -113,21 +113,21 @@ check.custom.obj <- function(env = parent.frame()) {
 check.custom.eval <- function(env = parent.frame()) {
   if (!is.null(env$params[['eval_metric']]) && !is.null(env$feval))
     stop("Setting evaluation metrics in 'params' and 'feval' at the same time is not allowed")
-  
+
   if (!is.null(env$feval) && typeof(env$feval) != 'closure')
     stop("'feval' must be a function")
-  
+
   # handle a situation when custom eval function was provided through params
   if (!is.null(env$params[['eval_metric']]) &&
       typeof(env$params$eval_metric) == 'closure') {
     env$feval <- env$params$eval_metric
     env$params$eval_metric <- NULL
   }
-  
+
   # require maximize to be set when custom feval and early stopping are used together
   if (!is.null(env$feval) &&
       is.null(env$maximize) && (
-        !is.null(env$early_stopping_rounds) || 
+        !is.null(env$early_stopping_rounds) ||
         has.callbacks(env$callbacks, 'cb.early.stop')))
     stop("Please set 'maximize' to indicate whether the evaluation metric needs to be maximized or not")
 }
@@ -154,15 +154,15 @@ xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
 
 
 # Evaluate one iteration.
-# Returns a named vector of evaluation metrics 
+# Returns a named vector of evaluation metrics
 # with the names in a 'datasetname-metricname' format.
 xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
   if (!identical(class(booster_handle), "xgb.Booster.handle"))
     stop("class of booster_handle must be xgb.Booster.handle")
 
-  if (length(watchlist) == 0) 
+  if (length(watchlist) == 0)
     return(NULL)
-  
+
   evnames <- names(watchlist)
   if (is.null(feval)) {
     msg <- .Call(XGBoosterEvalOneIter_R, booster_handle, as.integer(iter), watchlist, as.list(evnames))
@@ -189,7 +189,7 @@ xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
 
 # Generates random (stratified if needed) CV folds
 generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
-  
+
   # cannot do it for rank
   if (exists('objective', where = params) &&
       is.character(params$objective) &&
@@ -209,13 +209,14 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
     if (exists('objective', where = params) &&
         is.character(params$objective)) {
       # If 'objective' provided in params, assume that y is a classification label
-      # unless objective is reg:linear
+      # unless objective is reg:squarederror
-      if (params$objective != 'reg:linear')
+      if (params$objective != 'reg:squarederror')
         y <- factor(y)
     } else {
-      # If no 'objective' given in params, it means that user either wants to use
+      # If no 'objective' given in params, it means that user either wants to
-      # the default 'reg:linear' objective or has provided a custom obj function.
+      # use the default 'reg:squarederror' objective or has provided a custom
-      # Here, assume classification setting when y has 5 or less unique values:
+      # obj function.  Here, assume classification setting when y has 5 or less
+      # unique values:
       if (length(unique(y)) <= 5)
         y <- factor(y)
     }
@@ -293,22 +294,22 @@ xgb.createFolds <- function(y, k = 10)
 #
 
 #' Deprecation notices.
-#' 
+#'
 #' At this time, some of the parameter names were changed in order to make the code style more uniform.
 #' The deprecated parameters would be removed in the next release.
-#' 
+#'
 #' To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
-#' 
+#'
-#' A deprecation warning is shown when any of the deprecated parameters is used in a call. 
+#' A deprecation warning is shown when any of the deprecated parameters is used in a call.
-#' An additional warning is shown when there was a partial match to a deprecated parameter 
+#' An additional warning is shown when there was a partial match to a deprecated parameter
 #' (as R is able to partially match parameter names).
-#' 
+#'
 #' @name xgboost-deprecated
 NULL
 
 # Lookup table for the deprecated parameters bookkeeping
 depr_par_lut <- matrix(c(
-  'print.every.n', 'print_every_n', 
+  'print.every.n', 'print_every_n',
   'early.stop.round', 'early_stopping_rounds',
   'training.data', 'data',
   'with.stats', 'with_stats',

R-package/R/xgb.cv.R

@@ -1,12 +1,12 @@
 #' Cross Validation
-#' 
+#'
 #' The cross validation function of xgboost
-#' 
+#'
 #' @param params the list of parameters. Commonly used ones are:
 #' \itemize{
 #'   \item \code{objective} objective function, common ones are
 #'   \itemize{
-#'     \item \code{reg:linear} linear regression
+#'     \item \code{reg:squarederror} Regression with squared loss
 #'     \item \code{binary:logistic} logistic regression for classification
 #'   }
 #'   \item \code{eta} step size of each boosting step
@@ -18,12 +18,12 @@
 #'   See also demo/ for walkthrough example in R.
 #' @param data takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.
 #' @param nrounds the max number of iterations
-#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples. 
+#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
 #' @param label vector of response values. Should be provided only when data is an R-matrix.
-#' @param missing is only used when input is a dense matrix. By default is set to NA, which means 
+#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
-#'        that NA values should be considered as 'missing' by the algorithm. 
+#'        that NA values should be considered as 'missing' by the algorithm.
 #'        Sometimes, 0 or other extreme value might be used to represent missing values.
-#' @param prediction A logical value indicating whether to return the test fold predictions 
+#' @param prediction A logical value indicating whether to return the test fold predictions
 #'        from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in cross validation,
@@ -37,22 +37,22 @@
 #'   \item \code{aucpr} Area under PR curve
 #'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 #' }
-#' @param obj customized objective function. Returns gradient and second order 
+#' @param obj customized objective function. Returns gradient and second order
 #'        gradient with given prediction and dtrain.
-#' @param feval custimized evaluation function. Returns 
+#' @param feval custimized evaluation function. Returns
-#'        \code{list(metric='metric-name', value='metric-value')} with given 
+#'        \code{list(metric='metric-name', value='metric-value')} with given
 #'        prediction and dtrain.
-#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified 
+#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
 #'        by the values of outcome labels.
 #' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
-#'        (each element must be a vector of test fold's indices). When folds are supplied, 
+#'        (each element must be a vector of test fold's indices). When folds are supplied,
 #'        the \code{nfold} and \code{stratified} parameters are ignored.
 #' @param verbose \code{boolean}, print the statistics during the process
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the 
+#'        Default is 1 which means all messages are printed. This parameter is passed to the
 #'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered. 
+#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance 
+#'        If set to an integer \code{k}, training with a validation set will stop if the performance
 #'        doesn't improve for \code{k} rounds.
 #'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
 #' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
@@ -60,46 +60,46 @@
 #'        When it is \code{TRUE}, it means the larger the evaluation score the better.
 #'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
 #' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
+#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-#'        parameters' values. User can provide either existing or their own callback methods in order 
+#'        parameters' values. User can provide either existing or their own callback methods in order
 #'        to customize the training process.
 #' @param ... other parameters to pass to \code{params}.
-#' 
+#'
-#' @details 
+#' @details
-#' The original sample is randomly partitioned into \code{nfold} equal size subsamples. 
+#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
-#' 
+#'
-#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data. 
+#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
-#' 
+#'
 #' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
-#' 
+#'
 #' All observations are used for both training and validation.
-#' 
+#'
 #' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
 #'
-#' @return 
+#' @return
 #' An object of class \code{xgb.cv.synchronous} with the following elements:
 #' \itemize{
 #'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
+#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or 
+#'   \item \code{callbacks} callback functions that were either automatically assigned or
 #'         explicitly passed.
 #'   \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
-#'         first column corresponding to iteration number and the rest corresponding to the 
+#'         first column corresponding to iteration number and the rest corresponding to the
 #'         CV-based evaluation means and standard deviations for the training and test CV-sets.
 #'         It is created by the \code{\link{cb.evaluation.log}} callback.
 #'   \item \code{niter} number of boosting iterations.
 #'   \item \code{nfeatures} number of features in training data.
-#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds} 
+#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
 #'         parameter or randomly generated.
 #'   \item \code{best_iteration} iteration number with the best evaluation metric value
 #'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
+#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
 #'         which could further be used in \code{predict} method
 #'         (only available with early stopping).
-#'   \item \code{pred} CV prediction values available when \code{prediction} is set. 
+#'   \item \code{pred} CV prediction values available when \code{prediction} is set.
 #'         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-#'   \item \code{models} a liost of the CV folds' models. It is only available with the explicit 
+#'   \item \code{models} a liost of the CV folds' models. It is only available with the explicit
 #'         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 #' }
 #'
@@ -110,32 +110,32 @@
 #'                   max_depth = 3, eta = 1, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#' 
+#'
 #' @export
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
                    prediction = FALSE, showsd = TRUE, metrics=list(),
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, 
+                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
                    verbose = TRUE, print_every_n=1L,
                    early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
 
   check.deprecation(...)
-  
+
   params <- check.booster.params(params, ...)
   # TODO: should we deprecate the redundant 'metrics' parameter?
   for (m in metrics)
     params <- c(params, list("eval_metric" = m))
-  
+
   check.custom.obj()
   check.custom.eval()
 
   #if (is.null(params[['eval_metric']]) && is.null(feval))
   #  stop("Either 'eval_metric' or 'feval' must be provided for CV")
-  
+
   # Check the labels
   if ( (inherits(data, 'xgb.DMatrix') && is.null(getinfo(data, 'label'))) ||
        (!inherits(data, 'xgb.DMatrix') && is.null(label)))
     stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
-  
+
   # CV folds
   if(!is.null(folds)) {
     if(!is.list(folds) || length(folds) < 2)
@@ -146,7 +146,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
       stop("'nfold' must be > 1")
     folds <- generate.cv.folds(nfold, nrow(data), stratified, label, params)
   }
-  
+
   # Potential TODO: sequential CV
   #if (strategy == 'sequential')
   #  stop('Sequential CV strategy is not yet implemented')
@@ -166,7 +166,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   stop_condition <- FALSE
   if (!is.null(early_stopping_rounds) &&
       !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
+    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
                                                  maximize = maximize, verbose = verbose))
   }
   # CV-predictions callback
@@ -177,7 +177,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
 
-  
+
   # create the booster-folds
   dall <- xgb.get.DMatrix(data, label, missing)
   bst_folds <- lapply(seq_along(folds), function(k) {
@@ -197,12 +197,12 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # those are fixed for CV (no training continuation)
   begin_iteration <- 1
   end_iteration <- nrounds
-  
+
   # synchronous CV boosting: run CV folds' models within each iteration
   for (iteration in begin_iteration:end_iteration) {
-    
+
     for (f in cb$pre_iter) f()
-    
+
     msg <- lapply(bst_folds, function(fd) {
       xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj)
       xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, feval)
@@ -210,9 +210,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     msg <- simplify2array(msg)
     bst_evaluation <- rowMeans(msg)
     bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
-    
+
     for (f in cb$post_iter) f()
-    
+
     if (stop_condition) break
   }
   for (f in cb$finalize) f(finalize = TRUE)
@@ -236,17 +236,17 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 
 
 #' Print xgb.cv result
-#' 
+#'
 #' Prints formatted results of \code{xgb.cv}.
-#' 
+#'
 #' @param x an \code{xgb.cv.synchronous} object
 #' @param verbose whether to print detailed data
 #' @param ... passed to \code{data.table.print}
-#' 
+#'
 #' @details
-#' When not verbose, it would only print the evaluation results, 
+#' When not verbose, it would only print the evaluation results,
 #' including the best iteration (when available).
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
@@ -254,13 +254,13 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#' 
+#'
 #' @rdname print.xgb.cv
 #' @method print xgb.cv.synchronous
 #' @export
 print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
   cat('##### xgb.cv ', length(x$folds), '-folds\n', sep = '')
-  
+
   if (verbose) {
     if (!is.null(x$call)) {
       cat('call:\n  ')
@@ -268,8 +268,8 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
     if (!is.null(x$params)) {
       cat('params (as set within xgb.cv):\n')
-      cat( '  ', 
+      cat( '  ',
-           paste(names(x$params), 
+           paste(names(x$params),
                  paste0('"', unlist(x$params), '"'),
                  sep = ' = ', collapse = ', '), '\n', sep = '')
     }
@@ -280,9 +280,9 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
         print(x)
       })
     }
-    
+
     for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
-      if (is.null(x[[n]])) 
+      if (is.null(x[[n]]))
         next
       cat(n, ': ', x[[n]], '\n', sep = '')
     }
@@ -293,10 +293,10 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
   }
 
-  if (verbose) 
+  if (verbose)
     cat('evaluation_log:\n')
   print(x$evaluation_log, row.names = FALSE, ...)
-  
+
   if (!is.null(x$best_iteration)) {
     cat('Best iteration:\n')
     print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)

R-package/R/xgb.train.R

@@ -42,7 +42,7 @@
 #' \itemize{
 #' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
 #'   \itemize{
-#'     \item \code{reg:linear} linear regression (Default).
+#'     \item \code{reg:squarederror} Regression with squared loss (Default).
 #'     \item \code{reg:logistic} logistic regression.
 #'     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
 #'     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.

R-package/man/xgb.cv.Rd

@@ -16,7 +16,7 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
 \itemize{
   \item \code{objective} objective function, common ones are
   \itemize{
-    \item \code{reg:linear} linear regression
+    \item \code{reg:squarederror} Regression with squared loss.
     \item \code{binary:logistic} logistic regression for classification
   }
   \item \code{eta} step size of each boosting step

R-package/man/xgb.train.Rd

@@ -56,7 +56,7 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
 \itemize{
 \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
   \itemize{
-    \item \code{reg:linear} linear regression (Default).
+    \item \code{reg:squarederror} Regression with squared loss (Default).
     \item \code{reg:logistic} logistic regression.
     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
@@ -210,7 +210,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 watchlist <- list(train = dtrain, eval = dtest)
 
 ## A simple xgb.train example:
-param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
+param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2, 
               objective = "binary:logistic", eval_metric = "auc")
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 
@@ -231,12 +231,12 @@ evalerror <- function(preds, dtrain) {
 
 # These functions could be used by passing them either:
 #  as 'objective' and 'eval_metric' parameters in the params list:
-param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
+param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2, 
               objective = logregobj, eval_metric = evalerror)
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 
 #  or through the ... arguments:
-param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
+param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2)
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
                  objective = logregobj, eval_metric = evalerror)
 
@@ -246,7 +246,7 @@ bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 
 
 ## An xgb.train example of using variable learning rates at each iteration:
-param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
+param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2,
               objective = "binary:logistic", eval_metric = "auc")
 my_etas <- list(eta = c(0.5, 0.1))
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,

R-package/tests/testthat/test_dmatrix.R

@@ -10,12 +10,12 @@ test_label <- agaricus.test$label[1:100]
 test_that("xgb.DMatrix: basic construction", {
   # from sparse matrix
   dtest1 <- xgb.DMatrix(test_data, label=test_label)
-  
+
-  # from dense matrix 
+  # from dense matrix
   dtest2 <- xgb.DMatrix(as.matrix(test_data), label=test_label)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
   expect_equal(dim(dtest1), dim(dtest2))
-  
+
   #from dense integer matrix
   int_data <- as.matrix(test_data)
   storage.mode(int_data) <- "integer"
@@ -33,7 +33,7 @@ test_that("xgb.DMatrix: saving, loading", {
   expect_output(dtest3 <- xgb.DMatrix(tmp_file, silent = TRUE), NA)
   unlink(tmp_file)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest3, 'label'))
-  
+
   # from a libsvm text file
   tmp <- c("0 1:1 2:1","1 3:1","0 1:1")
   tmp_file <- 'tmp.libsvm'
@@ -49,7 +49,7 @@ test_that("xgb.DMatrix: getinfo & setinfo", {
   expect_true(setinfo(dtest, 'label', test_label))
   labels <- getinfo(dtest, 'label')
   expect_equal(test_label, getinfo(dtest, 'label'))
-  
+
   expect_true(length(getinfo(dtest, 'weight')) == 0)
   expect_true(length(getinfo(dtest, 'base_margin')) == 0)
 
@@ -57,10 +57,10 @@ test_that("xgb.DMatrix: getinfo & setinfo", {
   expect_true(setinfo(dtest, 'base_margin', test_label))
   expect_true(setinfo(dtest, 'group', c(50,50)))
   expect_error(setinfo(dtest, 'group', test_label))
-  
+
   # providing character values will give a warning
   expect_warning( setinfo(dtest, 'weight', rep('a', nrow(test_data))) )
-  
+
   # any other label should error
   expect_error(setinfo(dtest, 'asdf', test_label))
 })
@@ -71,7 +71,7 @@ test_that("xgb.DMatrix: slice, dim", {
   dsub1 <- slice(dtest, 1:42)
   expect_equal(nrow(dsub1), 42)
   expect_equal(ncol(dsub1), ncol(test_data))
-  
+
   dsub2 <- dtest[1:42,]
   expect_equal(dim(dtest), dim(test_data))
   expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))

R-package/tests/testthat/test_interactions.R

@@ -98,7 +98,7 @@ test_that("SHAP contribution values are not NAN", {
   fit <- xgboost(
     verbose = 0,
     params = list(
-      objective = "reg:linear",
+      objective = "reg:squarederror",
       eval_metric = "rmse"),
     data = as.matrix(subset(d, fold == 2)[, ivs]),
     label = subset(d, fold == 2)$y,

demo/regression/README.md

@@ -6,9 +6,9 @@ Using XGBoost for regression is very similar to using it for binary classificati
 The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
 ```
 # General parameter
-# this is the only difference with classification, use reg:linear to do linear regression
+# this is the only difference with classification, use reg:squarederror to do linear regression
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 ...
 
 ```

demo/regression/machine.conf

@@ -1,9 +1,9 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:linear to do linear classification
+# this is the only difference with classification, use reg:squarederror to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 
 # Tree Booster Parameters
 # step size shrinkage

demo/yearpredMSD/yearpredMSD.conf

@@ -1,17 +1,17 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:linear to do linear classification
+# this is the only difference with classification, use reg:squarederror to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 
 # Tree Booster Parameters
 # step size shrinkage
 eta = 1.0
 # minimum loss reduction required to make a further partition
-gamma = 1.0 
+gamma = 1.0
 # minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1 
+min_child_weight = 1
 # maximum depth of a tree
 max_depth = 5
 
@@ -20,11 +20,10 @@ base_score = 2001
 # the number of round to do boosting
 num_round = 100
 # 0 means do not save any model except the final round model
-save_period = 0 
+save_period = 0
 # The path of training data
 data = "yearpredMSD.libsvm.train"
 # The path of validation data, used to monitor training process, here [test] sets name of the validation set
 eval[test] = "yearpredMSD.libsvm.test"
-# The path of test data 
+# The path of test data
 #test:data = "yearpredMSD.libsvm.test"
-

doc/gpu/index.rst

@@ -92,7 +92,7 @@ Most of the objective functions implemented in XGBoost can be run on GPU.  Follo
 +-----------------+-------------+
 | Objectives      | GPU support |
 +-----------------+-------------+
-| reg:linear      | |tick|      |
+| reg:squarederror| |tick|      |
 +-----------------+-------------+
 | reg:logistic    | |tick|      |
 +-----------------+-------------+

doc/parameter.rst

@@ -293,9 +293,9 @@ Learning Task Parameters
 ************************
 Specify the learning task and the corresponding learning objective. The objective options are below:
 
-* ``objective`` [default=reg:linear]
+* ``objective`` [default=reg:squarederror]
 
-  - ``reg:linear``: linear regression
+  - ``reg:squarederror``: regression with squared loss
   - ``reg:logistic``: logistic regression
   - ``binary:logistic``: logistic regression for binary classification, output probability
   - ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation

doc/rf.rst

@@ -36,7 +36,7 @@ The following parameters must be set to enable random forest training.
 
 
 Other parameters should be set in a similar way they are set for gradient boosting. For
-instance, ``objective`` will typically be ``reg:linear`` for regression and
+instance, ``objective`` will typically be ``reg:squarederror`` for regression and
 ``binary:logistic`` for classification, ``lambda`` should be set according to a desired
 regularization weight, etc.
 

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala

@@ -24,8 +24,8 @@ private[spark] trait LearningTaskParams extends Params {
 
   /**
    * Specify the learning task and the corresponding learning objective.
-   * options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
+   * options: reg:squarederror, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
-   * multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear
+   * multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:squarederror
    */
   final val objective = new Param[String](this, "objective", "objective function used for " +
     s"training, options: {${LearningTaskParams.supportedObjective.mkString(",")}",
@@ -94,12 +94,12 @@ private[spark] trait LearningTaskParams extends Params {
 
   final def getMaximizeEvaluationMetrics: Boolean = $(maximizeEvaluationMetrics)
 
-  setDefault(objective -> "reg:linear", baseScore -> 0.5,
+  setDefault(objective -> "reg:squarederror", baseScore -> 0.5,
     trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0)
 }
 
 private[spark] object LearningTaskParams {
-  val supportedObjective = HashSet("reg:linear", "reg:logistic", "binary:logistic",
+  val supportedObjective = HashSet("reg:squarederror", "reg:logistic", "binary:logistic",
     "binary:logitraw", "count:poisson", "multi:softmax", "multi:softprob", "rank:pairwise",
     "rank:ndcg", "rank:map", "reg:gamma", "reg:tweedie")
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/PersistenceSuite.scala

@@ -96,7 +96,7 @@ class PersistenceSuite extends FunSuite with PerTest with BeforeAndAfterAll {
     val testDM = new DMatrix(Regression.test.iterator)
 
     val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> "10", "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> "10", "num_workers" -> numWorkers)
     val xgbr = new XGBoostRegressor(paramMap)
     val xgbrPath = new File(tempDir, "xgbr").getPath
     xgbr.write.overwrite().save(xgbrPath)

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressorSuite.scala

@@ -36,7 +36,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
       "eta" -> "1",
       "max_depth" -> "6",
       "silent" -> "1",
-      "objective" -> "reg:linear")
+      "objective" -> "reg:squarederror")
 
     val model1 = ScalaXGBoost.train(trainingDM, paramMap, round)
     val prediction1 = model1.predict(testDM)
@@ -69,7 +69,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
       "eta" -> "1",
       "max_depth" -> "6",
       "silent" -> "1",
-      "objective" -> "reg:linear",
+      "objective" -> "reg:squarederror",
       "num_round" -> round,
       "num_workers" -> numWorkers)
 
@@ -80,7 +80,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
       .setEta(1)
       .setMaxDepth(6)
       .setSilent(1)
-      .setObjective("reg:linear")
+      .setObjective("reg:squarederror")
       .setNumRound(round)
       .setNumWorkers(numWorkers)
       .fit(trainingDF)
@@ -108,7 +108,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("use weight") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
 
     val getWeightFromId = udf({id: Int => if (id == 0) 1.0f else 0.001f}, DataTypes.FloatType)
     val trainingDF = buildDataFrame(Regression.train)
@@ -123,7 +123,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("test predictionLeaf") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
     val training = buildDataFrame(Regression.train)
     val testDF = buildDataFrame(Regression.test)
     val groundTruth = testDF.count()
@@ -137,7 +137,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("test predictionLeaf with empty column name") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
     val training = buildDataFrame(Regression.train)
     val testDF = buildDataFrame(Regression.test)
     val xgb = new XGBoostRegressor(paramMap)
@@ -149,7 +149,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("test predictionContrib") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
     val training = buildDataFrame(Regression.train)
     val testDF = buildDataFrame(Regression.test)
     val groundTruth = testDF.count()
@@ -163,7 +163,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("test predictionContrib with empty column name") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
     val training = buildDataFrame(Regression.train)
     val testDF = buildDataFrame(Regression.test)
     val xgb = new XGBoostRegressor(paramMap)
@@ -175,7 +175,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
 
   test("test predictionLeaf and predictionContrib") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
+      "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
     val training = buildDataFrame(Regression.train)
     val testDF = buildDataFrame(Regression.test)
     val groundTruth = testDF.count()

src/objective/regression_obj.cu

@@ -128,8 +128,8 @@ class RegLossObj : public ObjFunction {
 // register the objective functions
 DMLC_REGISTER_PARAMETER(RegLossParam);
 
-XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
+XGBOOST_REGISTER_OBJECTIVE(SquaredLossRegression, "reg:squarederror")
-.describe("Linear regression.")
+.describe("Regression with squared error.")
 .set_body([]() { return new RegLossObj<LinearSquareLoss>(); });
 
 XGBOOST_REGISTER_OBJECTIVE(LogisticRegression, "reg:logistic")
@@ -145,7 +145,13 @@ XGBOOST_REGISTER_OBJECTIVE(LogisticRaw, "binary:logitraw")
           "before logistic transformation.")
 .set_body([]() { return new RegLossObj<LogisticRaw>(); });
 
-// Deprecated GPU functions
+// Deprecated functions
+XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
+.describe("Regression with squared error.")
+.set_body([]() {
+    LOG(WARNING) << "reg:linear is now deprecated in favor of reg:squarederror.";
+    return new RegLossObj<LinearSquareLoss>(); });
+
 XGBOOST_REGISTER_OBJECTIVE(GPULinearRegression, "gpu:reg:linear")
 .describe("Deprecated. Linear regression (computed on GPU).")
 .set_body([]() {

tests/cpp/objective/test_objective.cc

@@ -1,4 +1,5 @@
 // Copyright by Contributors
+#include <gtest/gtest.h>
 #include <xgboost/objective.h>
 
 #include "../helpers.h"
@@ -6,7 +7,7 @@
 TEST(Objective, UnknownFunction) {
   xgboost::ObjFunction* obj = nullptr;
   EXPECT_ANY_THROW(obj = xgboost::ObjFunction::Create("unknown_name"));
-  EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:linear"));
+  EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:squarederror"));
   if (obj) {
     delete obj;
   }

tests/cpp/objective/test_regression_obj.cc

@@ -1,12 +1,13 @@
 /*!
- * Copyright 2017-2018 XGBoost contributors
+ * Copyright 2017-2019 XGBoost contributors
  */
+#include <gtest/gtest.h>
 #include <xgboost/objective.h>
 
 #include "../helpers.h"
 
 TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
-  xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:linear");
+  xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:squarederror");
   std::vector<std::pair<std::string, std::string> > args;
   obj->Configure(args);
   CheckObjFunction(obj,

tests/python/regression_test_utilities.py

@@ -132,15 +132,16 @@ def run_suite(param, num_rounds=10, select_datasets=None, scale_features=False):
     Run the given parameters on a range of datasets. Objective and eval metric will be automatically set
     """
     datasets = [
-        Dataset("Boston", get_boston, "reg:linear", "rmse"),
+        Dataset("Boston", get_boston, "reg:squarederror", "rmse"),
         Dataset("Digits", get_digits, "multi:softmax", "merror"),
         Dataset("Cancer", get_cancer, "binary:logistic", "error"),
-        Dataset("Sparse regression", get_sparse, "reg:linear", "rmse"),
+        Dataset("Sparse regression", get_sparse, "reg:squarederror", "rmse"),
         Dataset("Sparse regression with weights", get_sparse_weights,
-                "reg:linear", "rmse", has_weights=True),
+                "reg:squarederror", "rmse", has_weights=True),
         Dataset("Small weights regression", get_small_weights,
-                "reg:linear", "rmse", has_weights=True),
+                "reg:squarederror", "rmse", has_weights=True),
-        Dataset("Boston External Memory", get_boston, "reg:linear", "rmse",
+        Dataset("Boston External Memory", get_boston,
+                "reg:squarederror", "rmse",
                 use_external_memory=True)
     ]
 

tests/python/test_basic.py

@@ -38,7 +38,7 @@ class TestBasic(unittest.TestCase):
     def test_basic(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                  'objective': 'binary:logistic'}
         # specify validations set to watch performance
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
@@ -85,7 +85,7 @@ class TestBasic(unittest.TestCase):
     def test_record_results(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                  'objective': 'binary:logistic'}
         # specify validations set to watch performance
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
@@ -102,7 +102,7 @@ class TestBasic(unittest.TestCase):
     def test_multiclass(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'num_class': 2}
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'num_class': 2}
         # specify validations set to watch performance
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
         num_round = 2
@@ -273,7 +273,7 @@ class TestBasic(unittest.TestCase):
 
     def test_cv(self):
         dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
 
         # return np.ndarray
@@ -283,7 +283,7 @@ class TestBasic(unittest.TestCase):
 
     def test_cv_no_shuffle(self):
         dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
 
         # return np.ndarray
@@ -294,7 +294,7 @@ class TestBasic(unittest.TestCase):
 
     def test_cv_explicit_fold_indices(self):
         dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective':
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
                   'binary:logistic'}
         folds = [
             # Train        Test
@@ -310,7 +310,7 @@ class TestBasic(unittest.TestCase):
 
     def test_cv_explicit_fold_indices_labels(self):
         params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
-                  'reg:linear'}
+                  'reg:squarederror'}
         N = 100
         F = 3
         dm = xgb.DMatrix(data=np.random.randn(N, F), label=np.arange(N))

tests/python/test_basic_models.py

@@ -11,7 +11,7 @@ rng = np.random.RandomState(1994)
 
 class TestModels(unittest.TestCase):
     def test_glm(self):
-        param = {'silent': 1, 'objective': 'binary:logistic',
+        param = {'verbosity': 0, 'objective': 'binary:logistic',
                  'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1, 'nthread': 1}
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
         num_round = 4
@@ -26,7 +26,7 @@ class TestModels(unittest.TestCase):
     def test_dart(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
-        param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'silent': False}
+        param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'verbosity': 1}
         # specify validations set to watch performance
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
         num_round = 2
@@ -51,7 +51,7 @@ class TestModels(unittest.TestCase):
 
         # check whether sample_type and normalize_type work
         num_round = 50
-        param['silent'] = True
+        param['verbosity'] = 0
         param['learning_rate'] = 0.1
         param['rate_drop'] = 0.1
         preds_list = []
@@ -74,7 +74,8 @@ class TestModels(unittest.TestCase):
 
         # learning_rates as a list
         # init eta with 0 to check whether learning_rates work
-        param = {'max_depth': 2, 'eta': 0, 'silent': 1, 'objective': 'binary:logistic'}
+        param = {'max_depth': 2, 'eta': 0, 'verbosity': 0,
+                 'objective': 'binary:logistic'}
         evals_result = {}
         bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
                         evals_result=evals_result)
@@ -84,7 +85,8 @@ class TestModels(unittest.TestCase):
         assert eval_errors[0] > eval_errors[-1]
 
         # init learning_rate with 0 to check whether learning_rates work
-        param = {'max_depth': 2, 'learning_rate': 0, 'silent': 1, 'objective': 'binary:logistic'}
+        param = {'max_depth': 2, 'learning_rate': 0, 'verbosity': 0,
+                 'objective': 'binary:logistic'}
         evals_result = {}
         bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
                         evals_result=evals_result)
@@ -94,7 +96,7 @@ class TestModels(unittest.TestCase):
         assert eval_errors[0] > eval_errors[-1]
 
         # check if learning_rates override default value of eta/learning_rate
-        param = {'max_depth': 2, 'silent': 1, 'objective': 'binary:logistic'}
+        param = {'max_depth': 2, 'verbosity': 0, 'objective': 'binary:logistic'}
         evals_result = {}
         bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0, 0, 0, 0],
                         evals_result=evals_result)
@@ -111,7 +113,7 @@ class TestModels(unittest.TestCase):
         assert isinstance(bst, xgb.core.Booster)
 
     def test_custom_objective(self):
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1}
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0}
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
         num_round = 2
 
@@ -152,7 +154,8 @@ class TestModels(unittest.TestCase):
 
     def test_multi_eval_metric(self):
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-        param = {'max_depth': 2, 'eta': 0.2, 'silent': 1, 'objective': 'binary:logistic'}
+        param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 0,
+                 'objective': 'binary:logistic'}
         param['eval_metric'] = ["auc", "logloss", 'error']
         evals_result = {}
         bst = xgb.train(param, dtrain, 4, watchlist, evals_result=evals_result)
@@ -161,7 +164,7 @@ class TestModels(unittest.TestCase):
         assert set(evals_result['eval'].keys()) == {'auc', 'error', 'logloss'}
 
     def test_fpreproc(self):
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                  'objective': 'binary:logistic'}
         num_round = 2
 
@@ -175,7 +178,7 @@ class TestModels(unittest.TestCase):
                metrics={'auc'}, seed=0, fpreproc=fpreproc)
 
     def test_show_stdv(self):
-        param = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                  'objective': 'binary:logistic'}
         num_round = 2
         xgb.cv(param, dtrain, num_round, nfold=5,

tests/python/test_early_stopping.py

@@ -52,7 +52,7 @@ class TestEarlyStopping(unittest.TestCase):
         X = digits['data']
         y = digits['target']
         dm = xgb.DMatrix(X, label=y)
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
 
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,

tests/python/test_eval_metrics.py

@@ -9,25 +9,25 @@ rng = np.random.RandomState(1337)
 
 class TestEvalMetrics(unittest.TestCase):
     xgb_params_01 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'eval_metric': 'error'
     }
 
     xgb_params_02 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'eval_metric': ['error']
     }
 
     xgb_params_03 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'eval_metric': ['rmse', 'error']
     }
 
     xgb_params_04 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'eval_metric': ['error', 'rmse']
     }

tests/python/test_interaction_constraints.py

@@ -18,7 +18,7 @@ class TestInteractionConstraints(unittest.TestCase):
         X = np.column_stack((x1, x2, x3))
         dtrain = xgboost.DMatrix(X, label=y)
 
-        params = {'max_depth': 3, 'eta': 0.1, 'nthread': 2, 'silent': 1,
+        params = {'max_depth': 3, 'eta': 0.1, 'nthread': 2, 'verbosity': 0,
                   'interaction_constraints': '[[0, 1]]'}
         num_boost_round = 100
         # Fit a model that only allows interaction between x1 and x2

tests/python/test_linear.py

@@ -30,7 +30,7 @@ def xgb_get_weights(bst):
 
 def assert_regression_result(results, tol):
     regression_results = [r for r in results if
-                          r["param"]["objective"] == "reg:linear"]
+                          r["param"]["objective"] == "reg:squarederror"]
     for res in regression_results:
         X = scale(res["dataset"].X,
                   with_mean=isinstance(res["dataset"].X, np.ndarray))
@@ -52,7 +52,7 @@ def assert_regression_result(results, tol):
 # TODO: More robust classification tests
 def assert_classification_result(results):
     classification_results = [r for r in results if
-                              r["param"]["objective"] != "reg:linear"]
+                              r["param"]["objective"] != "reg:squarederror"]
     for res in classification_results:
         # Check accuracy  is reasonable
         assert res["eval"][-1] < 0.5, (res["dataset"].name, res["eval"][-1])

tests/python/test_parse_tree.py

@@ -16,7 +16,8 @@ class TestTreesToDataFrame(unittest.TestCase):
 
     def build_model(self, max_depth, num_round):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
-        param = {'max_depth': max_depth, 'objective': 'binary:logistic', 'silent': False}
+        param = {'max_depth': max_depth, 'objective': 'binary:logistic',
+                 'verbosity': 1}
         num_round = num_round
         bst = xgb.train(param, dtrain, num_round)
         return bst

tests/python/test_shap.py

@@ -51,7 +51,7 @@ class TestSHAP(unittest.TestCase):
 
         def fn(max_depth, num_rounds):
             # train
-            params = {'max_depth': max_depth, 'eta': 1, 'silent': 1}
+            params = {'max_depth': max_depth, 'eta': 1, 'verbosity': 0}
             bst = xgb.train(params, dtrain, num_boost_round=num_rounds)
 
             # predict

tests/python/test_sparse_dmatrix.py

@@ -4,7 +4,7 @@ from scipy.sparse import rand
 
 rng = np.random.RandomState(1)
 
-param = {'max_depth': 3, 'objective': 'binary:logistic', 'silent': 1}
+param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
 
 
 def test_sparse_dmatrix_csr():

tests/python/test_training_continuation.py

@@ -11,18 +11,18 @@ class TestTrainingContinuation(unittest.TestCase):
     num_parallel_tree = 3
 
     xgb_params_01 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
     }
 
     xgb_params_02 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'num_parallel_tree': num_parallel_tree
     }
 
     xgb_params_03 = {
-        'silent': 1,
+        'verbosity': 0,
         'nthread': 1,
         'num_class': 5,
         'num_parallel_tree': num_parallel_tree

tests/python/test_tree_regularization.py

@@ -10,7 +10,8 @@ train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
 class TestTreeRegularization(unittest.TestCase):
     def test_alpha(self):
         params = {
-            'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
+            'tree_method': 'exact', 'verbosity': 0,
+            'objective': 'reg:squarederror',
             'eta': 1,
             'lambda': 0,
             'alpha': 0.1
@@ -27,7 +28,8 @@ class TestTreeRegularization(unittest.TestCase):
 
     def test_lambda(self):
         params = {
-            'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
+            'tree_method': 'exact', 'verbosity': 0,
+            'objective': 'reg:squarederror',
             'eta': 1,
             'lambda': 1,
             'alpha': 0
@@ -44,7 +46,8 @@ class TestTreeRegularization(unittest.TestCase):
 
     def test_alpha_and_lambda(self):
         params = {
-            'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
+            'tree_method': 'exact', 'verbosity': 1,
+            'objective': 'reg:squarederror',
             'eta': 1,
             'lambda': 1,
             'alpha': 0.1

tests/python/test_updaters.py

@@ -33,7 +33,7 @@ class TestUpdaters(unittest.TestCase):
                           'max_bin': [2, 256],
                           'grow_policy': ['depthwise', 'lossguide'],
                           'max_leaves': [64, 0],
-                          'silent': [1]}
+                          'verbosity': [0]}
         for param in parameter_combinations(variable_param):
             result = run_suite(param)
             assert_results_non_increasing(result, 1e-2)
@@ -45,7 +45,7 @@ class TestUpdaters(unittest.TestCase):
         ag_param = {'max_depth': 2,
                     'tree_method': 'hist',
                     'eta': 1,
-                    'silent': 1,
+                    'verbosity': 0,
                     'objective': 'binary:logistic',
                     'eval_metric': 'auc'}
         hist_res = {}

tests/python/test_with_pandas.py

@@ -120,7 +120,7 @@ class TestPandas(unittest.TestCase):
 
     def test_cv_as_pandas(self):
         dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
 
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
@@ -143,19 +143,19 @@ class TestPandas(unittest.TestCase):
                         u'train-error-mean', u'train-error-std'])
         assert cv.columns.equals(exp)
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic', 'eval_metric': 'auc'}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
         assert 'eval_metric' in params
         assert 'auc' in cv.columns[0]
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic', 'eval_metric': ['auc']}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
         assert 'eval_metric' in params
         assert 'auc' in cv.columns[0]
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic', 'eval_metric': ['auc']}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
                     as_pandas=True, early_stopping_rounds=1)
@@ -163,19 +163,19 @@ class TestPandas(unittest.TestCase):
         assert 'auc' in cv.columns[0]
         assert cv.shape[0] < 10
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
                     as_pandas=True, metrics='auc')
         assert 'auc' in cv.columns[0]
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic'}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
                     as_pandas=True, metrics=['auc'])
         assert 'auc' in cv.columns[0]
 
-        params = {'max_depth': 2, 'eta': 1, 'silent': 1,
+        params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
                   'objective': 'binary:logistic', 'eval_metric': ['auc']}
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
                     as_pandas=True, metrics='error')

tests/python/test_with_sklearn.py

@@ -603,7 +603,8 @@ def test_RFECV():
     # Regression
     X, y = load_boston(return_X_y=True)
     bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
-                            n_estimators=10, n_jobs=1, objective='reg:linear',
+                            n_estimators=10, n_jobs=1,
+                            objective='reg:squarederror',
                             random_state=0, verbosity=0)
     rfecv = RFECV(
         estimator=bst, step=1, cv=3, scoring='neg_mean_squared_error')