diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
index f9b209547a6a..6ef2c333b35d 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -1,3 +1,7 @@
+VERBOSITY <- as.integer(
+  Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
+)
+
 context("lightgbm()")
 
 ON_WINDOWS <- .Platform$OS.type == "windows"
@@ -337,6 +341,7 @@ test_that("training continuation works", {
     , metric = "binary_logloss"
     , num_leaves = 5L
     , learning_rate = 1.0
+    , verbose = VERBOSITY
   )
 
   # train for 10 consecutive iterations
@@ -538,6 +543,7 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
       objective = "binary"
       , metric = metrics
       , learning_rate = 1.0
+      , verbose = VERBOSITY
     )
     , valids = list(
       "train" = lgb.Dataset(
@@ -557,7 +563,11 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
 
 test_that("lgb.train() rejects negative or 0 value passed to nrounds", {
   dtrain <- lgb.Dataset(train$data, label = train$label)
-  params <- list(objective = "regression", metric = "l2,l1")
+  params <- list(
+    objective = "regression"
+    , metric = "l2,l1"
+    , verbose = VERBOSITY
+  )
   for (nround_value in c(-10L, 0L)) {
     expect_error({
       bst <- lgb.train(
@@ -585,6 +595,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
       , metric = "l2"
       , num_leaves = 5L
       , save_name = tempfile(fileext = ".model")
+      , verbose = VERBOSITY
     )
   )
 
@@ -600,6 +611,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
       , num_leaves = 5L
       , nrounds = nrounds
       , save_name = tempfile(fileext = ".model")
+      , verbose = VERBOSITY
     )
   )
 
@@ -616,6 +628,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
       , num_leaves = 5L
       , nrounds = nrounds
       , save_name = tempfile(fileext = ".model")
+      , verbose = VERBOSITY
     )
   )
 
@@ -651,7 +664,11 @@ test_that("lgb.train() throws an informative error if 'data' is not an lgb.Datas
   for (val in bad_values) {
     expect_error({
       bst <- lgb.train(
-        params = list(objective = "regression", metric = "l2,l1")
+        params = list(
+            objective = "regression"
+            , metric = "l2,l1"
+            , verbose = VERBOSITY
+        )
         , data = val
         , 10L
       )
@@ -666,7 +683,11 @@ test_that("lgb.train() throws an informative error if 'valids' is not a list of
   )
   expect_error({
     bst <- lgb.train(
-      params = list(objective = "regression", metric = "l2,l1")
+      params = list(
+        objective = "regression"
+        , metric = "l2,l1"
+        , verbose = VERBOSITY
+      )
       , data = lgb.Dataset(train$data, label = train$label)
       , 10L
       , valids = valids
@@ -681,7 +702,11 @@ test_that("lgb.train() errors if 'valids' is a list of lgb.Dataset objects but s
   )
   expect_error({
     bst <- lgb.train(
-      params = list(objective = "regression", metric = "l2,l1")
+      params = list(
+        objective = "regression"
+        , metric = "l2,l1"
+        , verbose = VERBOSITY
+      )
       , data = lgb.Dataset(train$data, label = train$label)
       , 10L
       , valids = valids
@@ -696,7 +721,11 @@ test_that("lgb.train() throws an informative error if 'valids' contains lgb.Data
   )
   expect_error({
     bst <- lgb.train(
-      params = list(objective = "regression", metric = "l2,l1")
+      params = list(
+        objective = "regression"
+        , metric = "l2,l1"
+        , verbose = VERBOSITY
+    )
       , data = lgb.Dataset(train$data, label = train$label)
       , 10L
       , valids = valids
@@ -715,6 +744,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
     objective = "binary"
     , metric = "binary_error"
     , force_col_wise = TRUE
+    , verbose = VERBOSITY
   )
   bst_col_wise <- lgb.train(
     params = params
@@ -726,6 +756,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
     objective = "binary"
     , metric = "binary_error"
     , force_row_wise = TRUE
+    , verbose = VERBOSITY
   )
   bst_row_wise <- lgb.train(
     params = params
@@ -764,6 +795,7 @@ test_that("lgb.train() works as expected with sparse features", {
       objective = "binary"
       , min_data = 1L
       , min_data_in_bin = 1L
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -804,6 +836,7 @@ test_that("lgb.train() works with early stopping for classification", {
     params = list(
       objective = "binary"
       , metric = "binary_error"
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -827,6 +860,7 @@ test_that("lgb.train() works with early stopping for classification", {
       objective = "binary"
       , metric = "binary_error"
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -875,6 +909,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
       params = list(
         objective = "binary"
         , metric = "binary_error"
+        , verbose = VERBOSITY
       )
       , data = dtrain
       , nrounds = nrounds
@@ -898,6 +933,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
         objective = "binary"
         , metric = "binary_error"
         , n_iter_no_change = value
+        , verbose = VERBOSITY
       )
       , data = dtrain
       , nrounds = nrounds
@@ -937,6 +973,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
       , metric = "auc"
       , max_depth = 3L
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -950,6 +987,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
       , metric = "binary_error"
       , max_depth = 3L
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -1008,6 +1046,7 @@ test_that("lgb.train() works with early stopping for regression", {
     params = list(
       objective = "regression"
       , metric = "rmse"
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -1031,6 +1070,7 @@ test_that("lgb.train() works with early stopping for regression", {
       objective = "regression"
       , metric = "rmse"
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -1065,6 +1105,7 @@ test_that("lgb.train() does not stop early if early_stopping_rounds is not given
     params = list(
       objective = "regression"
       , metric = "None"
+      , verbose = VERBOSITY
     )
     , data = DTRAIN_RANDOM_REGRESSION
     , nrounds = nrounds
@@ -1108,12 +1149,14 @@ test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to
       objective = "regression"
       , metric = "None"
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , list(
       objective = "regression"
       , metric = "None"
       , early_stopping_rounds = early_stopping_rounds
       , first_metric_only = FALSE
+      , verbose = VERBOSITY
     )
   )
 
@@ -1176,6 +1219,7 @@ test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based
       , metric = "None"
       , early_stopping_rounds = early_stopping_rounds
       , first_metric_only = TRUE
+      , verbose = VERBOSITY
     )
     , data = DTRAIN_RANDOM_REGRESSION
     , nrounds = nrounds
@@ -1221,6 +1265,7 @@ test_that("lgb.train() works when a mixture of functions and strings are passed
     params = list(
       objective = "regression"
       , metric = "None"
+      , verbose = VERBOSITY
     )
     , data = DTRAIN_RANDOM_REGRESSION
     , nrounds = nrounds
@@ -1276,6 +1321,7 @@ test_that("lgb.train() works when a list of strings or a character vector is pas
       params = list(
         objective = "binary"
         , metric = "None"
+        , verbose = VERBOSITY
       )
       , data = DTRAIN_RANDOM_CLASSIFICATION
       , nrounds = nrounds
@@ -1312,6 +1358,7 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri
     params = list(
       objective = "binary"
       , metric = "binary_error"
+      , verbose = VERBOSITY
     )
     , data = DTRAIN_RANDOM_CLASSIFICATION
     , nrounds = nrounds
@@ -1343,6 +1390,7 @@ test_that("lgb.train() works when you give a function for eval", {
     params = list(
       objective = "binary"
       , metric = "None"
+      , verbose = VERBOSITY
     )
     , data = DTRAIN_RANDOM_CLASSIFICATION
     , nrounds = nrounds
@@ -1391,6 +1439,7 @@ test_that("lgb.train() works with early stopping for regression with a metric th
       )
       , min_data_in_bin = 5L
       , early_stopping_rounds = early_stopping_rounds
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = nrounds
@@ -1430,6 +1479,7 @@ test_that("lgb.train() supports non-ASCII feature names", {
     , obj = "regression"
     , params = list(
       metric = "rmse"
+      , verbose = VERBOSITY
     )
     , colnames = feature_names
   )
@@ -1512,6 +1562,7 @@ test_that("when early stopping is not activated, best_iter and best_score come f
     , metric = "rmse"
     , learning_rate = 1.5
     , num_leaves = 5L
+    , verbose = VERBOSITY
   )
 
   # example 1: two valids, neither are the training data
@@ -1671,6 +1722,7 @@ test_that("lightgbm.train() gives the correct best_score and best_iter for a met
       , metric = "auc"
       , learning_rate = 1.5
       , num_leaves = 5L
+      , verbose = VERBOSITY
     )
   )
   # note that "something-random-we-would-not-hardcode" was recognized as the training
@@ -1915,7 +1967,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
 
   params <- list(
     objective = "regression"
-    , verbose = -1L
+    , verbose = VERBOSITY
     , metric = "mse"
     , seed = 0L
     , num_leaves = 2L
@@ -1949,7 +2001,7 @@ test_that("lgb.train() w/ linear learner fails already-constructed dataset with
   set.seed(708L)
   params <- list(
     objective = "regression"
-    , verbose = -1L
+    , verbose = VERBOSITY
     , metric = "mse"
     , seed = 0L
     , num_leaves = 2L
@@ -1986,7 +2038,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
 
   params <- list(
     objective = "regression"
-    , verbose = -1L
+    , verbose = VERBOSITY
     , metric = "mse"
     , seed = 0L
     , num_leaves = 2L
@@ -2032,7 +2084,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
 
   params <- list(
     objective = "regression"
-    , verbose = -1L
+    , verbose = VERBOSITY
     , metric = "mse"
     , seed = 0L
     , num_leaves = 2L
diff --git a/R-package/tests/testthat/test_custom_objective.R b/R-package/tests/testthat/test_custom_objective.R
index 54f5c300907a..78eb28d9f1b4 100644
--- a/R-package/tests/testthat/test_custom_objective.R
+++ b/R-package/tests/testthat/test_custom_objective.R
@@ -1,3 +1,7 @@
+VERBOSITY <- as.integer(
+  Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
+)
+
 context("Test models with custom objective")
 
 data(agaricus.train, package = "lightgbm")
@@ -36,6 +40,7 @@ param <- list(
   , learning_rate = 1.0
   , objective = logregobj
   , metric = "auc"
+  , verbose = VERBOSITY
 )
 num_round <- 10L
 
@@ -50,6 +55,7 @@ test_that("using a custom objective, custom eval, and no other metrics works", {
     params = list(
       num_leaves = 8L
       , learning_rate = 1.0
+      , verbose = VERBOSITY
     )
     , data = dtrain
     , nrounds = 4L
diff --git a/R-package/tests/testthat/test_dataset.R b/R-package/tests/testthat/test_dataset.R
index 52515440d7fb..2fc41b28e2d2 100644
--- a/R-package/tests/testthat/test_dataset.R
+++ b/R-package/tests/testthat/test_dataset.R
@@ -1,3 +1,7 @@
+VERBOSITY <- as.integer(
+  Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
+)
+
 context("testing lgb.Dataset functionality")
 
 data(agaricus.train, package = "lightgbm")
@@ -368,6 +372,7 @@ test_that("lgb.Dataset: should be able to run lgb.train() immediately after usin
     , metric = "binary_logloss"
     , num_leaves = 5L
     , learning_rate = 1.0
+    , verbose = VERBOSITY
   )
 
   # should be able to train right away
diff --git a/R-package/tests/testthat/test_learning_to_rank.R b/R-package/tests/testthat/test_learning_to_rank.R
index d0966692f5ba..8a313d21065e 100644
--- a/R-package/tests/testthat/test_learning_to_rank.R
+++ b/R-package/tests/testthat/test_learning_to_rank.R
@@ -1,3 +1,7 @@
+VERBOSITY <- as.integer(
+  Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
+)
+
 context("Learning to rank")
 
 # numerical tolerance to use when checking metric values
@@ -25,6 +29,7 @@ test_that("learning-to-rank with lgb.train() works as expected", {
         , ndcg_at = ndcg_at
         , lambdarank_truncation_level = 3L
         , learning_rate = 0.001
+        , verbose = VERBOSITY
     )
     model <- lgb.train(
         params = params
diff --git a/R-package/tests/testthat/test_lgb.Booster.R b/R-package/tests/testthat/test_lgb.Booster.R
index 1b357f794468..7f3605648769 100644
--- a/R-package/tests/testthat/test_lgb.Booster.R
+++ b/R-package/tests/testthat/test_lgb.Booster.R
@@ -1,3 +1,7 @@
+VERBOSITY <- as.integer(
+  Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
+)
+
 context("Booster")
 
 ON_WINDOWS <- .Platform$OS.type == "windows"
@@ -12,7 +16,7 @@ test_that("Booster$finalize() should not fail", {
         , params = list(
             objective = "regression"
         )
-        , verbose = -1L
+        , verbose = VERBOSITY
         , nrounds = 3L
     )
     expect_true(lgb.is.Booster(bst))
@@ -65,6 +69,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
             , metric = "l2"
             , min_data = 1L
             , learning_rate = 1.0
+            , verbose = VERBOSITY
         )
         , data = dtrain
         , nrounds = 5L
@@ -98,6 +103,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
             , metric = "l2"
             , min_data = 1L
             , learning_rate = 1.0
+            , verbose = VERBOSITY
         )
         , data = dtrain
         , nrounds = 5L
@@ -133,6 +139,7 @@ test_that("lgb.load() gives the expected error messages given different incorrec
             objective = "binary"
             , num_leaves = 4L
             , learning_rate = 1.0
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -179,6 +186,7 @@ test_that("Loading a Booster from a text file works", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -221,6 +229,7 @@ test_that("boosters with linear models at leaves can be written to text file and
         data = dtrain
         , nrounds = 10L
         , params = params
+        , verbose = VERBOSITY
     )
     expect_true(lgb.is.Booster(bst))
 
@@ -254,6 +263,7 @@ test_that("Loading a Booster from a string works", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -289,7 +299,7 @@ test_that("Saving a large model to string should work", {
         )
         , nrounds = 500L
         , save_name = tempfile(fileext = ".model")
-        , verbose = -1L
+        , verbose = VERBOSITY
     )
 
     pred <- predict(bst, train$data)
@@ -333,7 +343,7 @@ test_that("Saving a large model to JSON should work", {
         )
         , nrounds = 200L
         , save_name = tempfile(fileext = ".model")
-        , verbose = -1L
+        , verbose = VERBOSITY
     )
 
     model_json <- bst$dump_model()
@@ -360,6 +370,7 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -396,6 +407,7 @@ test_that("Creating a Booster from a Dataset should work", {
     bst <- Booster$new(
         params = list(
             objective = "binary"
+            , verbose = VERBOSITY
         ),
         train_set = dtrain
     )
@@ -416,6 +428,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = nrounds
         , save_name = tempfile(fileext = ".model")
@@ -428,6 +441,9 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
     )
     bst_from_ds <- Booster$new(
         train_set = dtest
+        , params = list(
+            verbose = VERBOSITY
+        )
     )
     expect_true(lgb.is.Booster(bst))
     expect_equal(bst$current_iter(), nrounds)
@@ -449,6 +465,7 @@ test_that("Booster$eval() should work on a Dataset stored in a binary file", {
             objective = "regression"
             , metric = "l2"
             , num_leaves = 4L
+            , verbose = VERBOSITY
         )
         , data = dtrain
         , nrounds = 2L
@@ -505,6 +522,7 @@ test_that("Booster$rollback_one_iter() should work as expected", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = nrounds
         , save_name = tempfile(fileext = ".model")
@@ -539,6 +557,7 @@ test_that("Booster$update() passing a train_set works as expected", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = nrounds
         , save_name = tempfile(fileext = ".model")
@@ -562,6 +581,7 @@ test_that("Booster$update() passing a train_set works as expected", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = nrounds +  1L
         , save_name = tempfile(fileext = ".model")
@@ -587,6 +607,7 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = nrounds
         , save_name = tempfile(fileext = ".model")
@@ -614,6 +635,7 @@ test_that("Booster should store parameters and Booster$reset_parameter() should
         , metric = c("multi_logloss", "multi_error")
         , boosting = "gbdt"
         , num_class = 5L
+        , verbose = VERBOSITY
     )
     bst <- Booster$new(
         params = params
@@ -640,6 +662,7 @@ test_that("Booster$params should include dataset params, before and after Booste
         objective = "binary"
         , max_depth = 4L
         , bagging_fraction = 0.8
+        , verbose = VERBOSITY
     )
     bst <- Booster$new(
         params = params
@@ -651,6 +674,7 @@ test_that("Booster$params should include dataset params, before and after Booste
             objective = "binary"
             , max_depth = 4L
             , bagging_fraction = 0.8
+            , verbose = VERBOSITY
             , max_bin = 17L
         )
     )
@@ -661,6 +685,7 @@ test_that("Booster$params should include dataset params, before and after Booste
         objective = "binary"
         , max_depth = 4L
         , bagging_fraction = 0.9
+        , verbose = VERBOSITY
         , max_bin = 17L
     )
     expect_identical(ret_bst$params, expected_params)
@@ -680,6 +705,7 @@ test_that("Saving a model with different feature importance types works", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -735,6 +761,7 @@ test_that("Saving a model with unknown importance type fails", {
             num_leaves = 4L
             , learning_rate = 1.0
             , objective = "binary"
+            , verbose = VERBOSITY
         )
         , nrounds = 2L
         , save_name = tempfile(fileext = ".model")
@@ -770,7 +797,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
         )
         , data = dtrain
         , nrounds = nrounds
-        , verbose = 0L
+        , verbose = VERBOSITY
     )
 
     model_str <- bst$save_model_to_string()
@@ -787,7 +814,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
     expect_equal(sum(params_in_file == "[objective: regression]"), 1L)
 
     expect_equal(sum(grepl(pattern = "^\\[verbosity\\:", x = params_in_file)), 1L)
-    expect_equal(sum(params_in_file == "[verbosity: 0]"), 1L)
+    expect_equal(sum(params_in_file == sprintf("[verbosity: %i]", VERBOSITY)), 1L)
 
     # early stopping should be off by default
     expect_equal(sum(grepl(pattern = "^\\[early_stopping_round\\:", x = params_in_file)), 1L)
@@ -833,7 +860,7 @@ test_that("early_stopping, num_iterations are stored correctly in model string e
         , valids = list(
             "random_valid" = dvalid
         )
-        , verbose = 0L
+        , verbose = VERBOSITY
     )
 
     model_str <- bst$save_model_to_string()
@@ -864,7 +891,7 @@ test_that("Booster: method calls Booster with a null handle should raise an info
             , num_leaves = 8L
         )
         , data = dtrain
-        , verbose = -1L
+        , verbose = VERBOSITY
         , nrounds = 5L
         , valids = list(
             train = dtrain
@@ -937,7 +964,12 @@ test_that("Booster$new() using a Dataset with a null handle should raise an info
     rm(dtrain)
     dtrain <- readRDS(tmp_file)
     expect_error({
-        bst <- Booster$new(train_set = dtrain)
+        bst <- Booster$new(
+            train_set = dtrain
+            , params = list(
+                verbose = VERBOSITY
+            )
+        )
     }, regexp = "lgb.Booster: cannot create Booster handle")
 })
 
@@ -969,6 +1001,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
         , n_iter = n_iter
         , early_stopping_round = early_stopping_round
         , n_iter_no_change = n_iter_no_change
+        , verbose = VERBOSITY
     )
 
     cv_bst <- lgb.cv(
@@ -977,7 +1010,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
         , nrounds = nrounds_kwarg
         , early_stopping_rounds = early_stopping_round_kwarg
         , nfold = 3L
-        , verbose = 0L
+        , verbose = VERBOSITY
     )
 
     for (bst in cv_bst$boosters) {
@@ -1014,6 +1047,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
         objective = "binary"
         , max_depth = 4L
         , bagging_fraction = 0.8
+        , verbose = VERBOSITY
     )
     bst <- Booster$new(
         params = params
@@ -1029,6 +1063,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
             objective = "binary"
             , max_depth = 4L
             , bagging_fraction = 0.8
+            , verbose = VERBOSITY
             , max_bin = 17L
         )
     )
@@ -1049,6 +1084,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
         objective = "binary"
         , max_depth = 4L
         , bagging_fraction = 0.8
+        , verbose = VERBOSITY
     )
     bst <- Booster$new(
         params = params
@@ -1064,6 +1100,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
             objective = "binary"
             , max_depth = 4L
             , bagging_fraction = 0.8
+            , verbose = VERBOSITY
             , max_bin = 17L
         )
     )
@@ -1071,7 +1108,15 @@ test_that("params (including dataset params) should be stored in .rds file for B
 
 test_that("Handle is automatically restored when calling predict", {
     data(agaricus.train, package = "lightgbm")
-    bst <- lightgbm(agaricus.train$data, agaricus.train$label, nrounds = 5L, obj = "binary")
+    bst <- lightgbm(
+        agaricus.train$data
+        , agaricus.train$label
+        , nrounds = 5L
+        , obj = "binary"
+        , params = list(
+            verbose = VERBOSITY
+        )
+    )
     bst_file <- tempfile(fileext = ".rds")
     saveRDS(bst, file = bst_file)
 
@@ -1092,7 +1137,7 @@ test_that("boosters with linear models at leaves work with saveRDS.lgb.Booster a
 
     params <- list(
         objective = "regression"
-        , verbose = -1L
+        , verbose = VERBOSITY
         , metric = "mse"
         , seed = 0L
         , num_leaves = 2L
@@ -1129,7 +1174,7 @@ test_that("boosters with linear models at leaves can be written to RDS and re-lo
 
     params <- list(
         objective = "regression"
-        , verbose = -1L
+        , verbose = VERBOSITY
         , metric = "mse"
         , seed = 0L
         , num_leaves = 2L
@@ -1190,7 +1235,7 @@ test_that("Booster's print, show, and summary work correctly", {
         , data = lgb.Dataset(
             as.matrix(mtcars[, -1L])
             , label = mtcars$mpg)
-        , verbose = 0L
+        , verbose = VERBOSITY
         , nrounds = 5L
     )
     .check_methods_work(model)
@@ -1202,7 +1247,7 @@ test_that("Booster's print, show, and summary work correctly", {
             as.matrix(iris[, -5L])
             , label = as.numeric(factor(iris$Species)) - 1.0
         )
-        , verbose = 0L
+        , verbose = VERBOSITY
         , nrounds = 5L
     )
     .check_methods_work(model)
@@ -1235,7 +1280,7 @@ test_that("Booster's print, show, and summary work correctly", {
         )
         , obj = .logregobj
         , eval = .evalerror
-        , verbose = 0L
+        , verbose = VERBOSITY
         , nrounds = 5L
     )
 
@@ -1249,7 +1294,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
         , data = lgb.Dataset(
             as.matrix(mtcars[, -1L])
             , label = mtcars$mpg)
-        , verbose = 0L
+        , verbose = VERBOSITY
         , nrounds = 5L
     )
     ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)
@@ -1262,7 +1307,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
             as.matrix(iris[, -5L])
             , label = as.numeric(factor(iris$Species)) - 1.0
         )
-        , verbose = 0L
+        , verbose = VERBOSITY
         , nrounds = 5L
     )
     ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)