diff --git a/examples/plot_quantile_conformalized.py b/examples/plot_quantile_conformalized.py
index 34dc98a..e9c9e9f 100755
--- a/examples/plot_quantile_conformalized.py
+++ b/examples/plot_quantile_conformalized.py
@@ -23,6 +23,8 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
+alt.data_transformers.disable_max_rows()
+
 strategies = {
     "qrf": "Quantile Regression Forest (QRF)",
     "cqr": "Conformalized Quantile Regression (CQR)",
diff --git a/examples/plot_quantile_multioutput.py b/examples/plot_quantile_multioutput.py
index 81716be..2583df4 100755
--- a/examples/plot_quantile_multioutput.py
+++ b/examples/plot_quantile_multioutput.py
@@ -16,6 +16,8 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
+alt.data_transformers.disable_max_rows()
+
 np.random.seed(0)
 
 n_samples = 2500
diff --git a/examples/plot_quantile_vs_standard.py b/examples/plot_quantile_vs_standard.py
index 6aff07c..08f9d60 100755
--- a/examples/plot_quantile_vs_standard.py
+++ b/examples/plot_quantile_vs_standard.py
@@ -21,6 +21,8 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
+alt.data_transformers.disable_max_rows()
+
 rng = check_random_state(0)
 
 # Create right-skewed dataset.
diff --git a/examples/plot_treeshap_example.py b/examples/plot_treeshap_example.py
index 44aaeb7..f186dd7 100644
--- a/examples/plot_treeshap_example.py
+++ b/examples/plot_treeshap_example.py
@@ -22,6 +22,8 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
+alt.data_transformers.disable_max_rows()
+
 n_samples = 500
 test_idx = 0
 quantiles = list((np.arange(11) * 10) / 100)
@@ -38,8 +40,8 @@ def get_shap_values(qrf, X, quantile=0.5, **kwargs):
     # Use Tree SHAP to generate explanations.
     explainer = shap.TreeExplainer(model, X)
 
-    qrf_pred = qrf.predict(X.to_numpy(), quantiles=quantile, **kwargs)
-    rf_pred = qrf.predict(X.to_numpy(), quantiles="mean", aggregate_leaves_first=False)
+    qrf_pred = qrf.predict(X, quantiles=quantile, **kwargs)
+    rf_pred = qrf.predict(X, quantiles="mean", aggregate_leaves_first=False)
 
     scaling = 1.0 / len(qrf.estimators_)  # scale factor based on the number of estimators
     base_offset = qrf_pred - rf_pred  # difference between the QRF and RF (baseline) predictions