diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d2eee06f..4ee54f84 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -52,7 +52,7 @@ repos:
   rev: v2.3.1
   hooks:
     - id: mypy
-      additional_dependencies: [types-setuptools]
+      additional_dependencies: [types-setuptools, xgboost]
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.16.4
   hooks:
diff --git a/ops/conda_env/dev.yml b/ops/conda_env/dev.yml
index 37b55e4b..76441ef4 100644
--- a/ops/conda_env/dev.yml
+++ b/ops/conda_env/dev.yml
@@ -2,7 +2,7 @@ name: dev
 channels:
 - conda-forge
 dependencies:
-- python=3.12
+- python=3.13
 - numpy
 - scipy
 - pandas
@@ -13,7 +13,7 @@ dependencies:
 - coverage
 - codecov
 - ninja
-- lcov
+- lcov<2.0
 - cmake
 - llvm-openmp
 - cython
diff --git a/ops/cpp-python-coverage.sh b/ops/cpp-python-coverage.sh
index db5eb2f4..69aa550d 100755
--- a/ops/cpp-python-coverage.sh
+++ b/ops/cpp-python-coverage.sh
@@ -2,9 +2,6 @@
 
 set -euo pipefail
 
-echo "##[section]Installing lcov and Ninja..."
-sudo apt-get install lcov ninja-build
-
 echo "##[section]Building Treelite..."
 mkdir build/
 cd build/
diff --git a/python/treelite/sklearn/importer.py b/python/treelite/sklearn/importer.py
index 8b2c0bc9..1d7f3d94 100644
--- a/python/treelite/sklearn/importer.py
+++ b/python/treelite/sklearn/importer.py
@@ -431,9 +431,7 @@ def _import_hist_gradient_boosting(sklearn_model) -> Model:
                 feat_remapper[n_categorical + num_idx] = i
                 num_idx += 1
     else:
-        feat_remapper = np.arange(
-            start=0, stop=sklearn_model.n_features_in_, dtype=np.int32
-        )
+        feat_remapper = np.arange(0, stop=sklearn_model.n_features_in_, dtype=np.int32)
 
     n_categorical_splits = known_cat_bitsets.shape[0]
     n_trees = 0
diff --git a/tests/python/util.py b/tests/python/util.py
index f7dc7e8e..74a3258a 100644
--- a/tests/python/util.py
+++ b/tests/python/util.py
@@ -94,7 +94,9 @@ def to_categorical(
     cat_cols = (cat_cols * rough_n_categories).astype(int)
 
     # Mix categorical and numerical columns in a random order
-    new_col_idx = rng.choice(n_features, n_features, replace=False, shuffle=True)
+    new_col_idx = np.asarray(
+        rng.choice(n_features, n_features, replace=False, shuffle=True)
+    )
     df_cols = {}
     for icol in range(n_categorical):
         col = cat_cols[:, icol]