diff --git a/python-package/.pylintrc b/python-package/.pylintrc
index 342d98b79..b224023db 100644
--- a/python-package/.pylintrc
+++ b/python-package/.pylintrc
@@ -2,6 +2,8 @@
 
 ignore=tests
 
+extension-pkg-whitelist=numpy
+
 disiable=unexpected-special-method-signature,too-many-nested-blocks
 
 dummy-variables-rgx=(unused|)_.*
@@ -19,3 +21,6 @@ attr-naming-style=snake_case
 argument-naming-style=snake_case
 variable-naming-style=snake_case
 class-attribute-naming-style=snake_case
+
+# Allow single-letter variables
+variable-rgx=[a-zA-Z_][a-z0-9_]{0,30}$
diff --git a/tests/benchmark/benchmark_tree.py b/tests/benchmark/benchmark_tree.py
index d913ee769..4bde8fc68 100644
--- a/tests/benchmark/benchmark_tree.py
+++ b/tests/benchmark/benchmark_tree.py
@@ -1,65 +1,81 @@
-# pylint: skip-file
-import sys, argparse
-import xgboost as xgb
+"""Run benchmark on the tree booster."""
+
+import argparse
+import ast
+import time
+
 import numpy as np
 from sklearn.datasets import make_classification
 from sklearn.model_selection import train_test_split
-import time
-import ast
+import xgboost as xgb
 
-rng = np.random.RandomState(1994)
+RNG = np.random.RandomState(1994)
 
 
 def run_benchmark(args):
-
+    """Runs the benchmark."""
     try:
         dtest = xgb.DMatrix('dtest.dm')
         dtrain = xgb.DMatrix('dtrain.dm')
 
-        if not (dtest.num_col() == args.columns \
+        if not (dtest.num_col() == args.columns
                 and dtrain.num_col() == args.columns):
             raise ValueError("Wrong cols")
-        if not (dtest.num_row() == args.rows * args.test_size \
-                and dtrain.num_row() == args.rows * (1-args.test_size)):
+        if not (dtest.num_row() == args.rows * args.test_size
+                and dtrain.num_row() == args.rows * (1 - args.test_size)):
             raise ValueError("Wrong rows")
-    except:
-
+    except xgb.core.XGBoostError:
         print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
         print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
         tmp = time.time()
-        X, y = make_classification(args.rows, n_features=args.columns, n_redundant=0, n_informative=args.columns, n_repeated=0, random_state=7)
+        X, y = make_classification(args.rows, n_features=args.columns, n_redundant=0,
+                                   n_informative=args.columns, n_repeated=0, random_state=7)
         if args.sparsity < 1.0:
-           X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
+            X = np.array([[np.nan if RNG.uniform(0, 1) < args.sparsity else x for x in x_row]
+                          for x_row in X])
 
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
-        print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size,
+                                                            random_state=7)
+        print("Generate Time: %s seconds" % (str(time.time() - tmp)))
         tmp = time.time()
-        print ("DMatrix Start")
+        print("DMatrix Start")
         dtrain = xgb.DMatrix(X_train, y_train)
         dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
-        print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
+        print("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
 
         dtest.save_binary('dtest.dm')
         dtrain.save_binary('dtrain.dm')
 
     param = {'objective': 'binary:logistic'}
-    if args.params is not '':
+    if args.params != '':
         param.update(ast.literal_eval(args.params))
 
     param['tree_method'] = args.tree_method
     print("Training with '%s'" % param['tree_method'])
     tmp = time.time()
     xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
-    print ("Train Time: %s seconds" % (str(time.time() - tmp)))
+    print("Train Time: %s seconds" % (str(time.time() - tmp)))
 
-parser = argparse.ArgumentParser()
-parser.add_argument('--tree_method', default='gpu_hist')
-parser.add_argument('--sparsity', type=float, default=0.0)
-parser.add_argument('--rows', type=int, default=1000000)
-parser.add_argument('--columns', type=int, default=50)
-parser.add_argument('--iterations', type=int, default=500)
-parser.add_argument('--test_size', type=float, default=0.25)
-parser.add_argument('--params', default='', help='Provide additional parameters as a Python dict string, e.g. --params \"{\'max_depth\':2}\"')
-args = parser.parse_args()
 
-run_benchmark(args)
+def main():
+    """The main function.
+
+    Defines and parses command line arguments and calls the benchmark.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--tree_method', default='gpu_hist')
+    parser.add_argument('--sparsity', type=float, default=0.0)
+    parser.add_argument('--rows', type=int, default=1000000)
+    parser.add_argument('--columns', type=int, default=50)
+    parser.add_argument('--iterations', type=int, default=500)
+    parser.add_argument('--test_size', type=float, default=0.25)
+    parser.add_argument('--params', default='',
+                        help='Provide additional parameters as a Python dict string, e.g. --params '
+                             '\"{\'max_depth\':2}\"')
+    args = parser.parse_args()
+
+    run_benchmark(args)
+
+
+if __name__ == '__main__':
+    main()