diff --git a/examples/plot_pruning.py b/examples/plot_pruning.py
index b9d3fde..c329fdd 100644
--- a/examples/plot_pruning.py
+++ b/examples/plot_pruning.py
@@ -26,7 +26,7 @@
 from sklearn.linear_model import LogisticRegression
 
 data, labels = load_iris(return_X_y=True)
-baseline_lr = LogisticRegression(max_iter=110).fit(data, labels)
+baseline_lr = LogisticRegression(max_iter=1000).fit(data, labels)
 
 # %%
 # Random data pruning
@@ -40,7 +40,7 @@
 def _random_pruning(X, y, n_samples_to_select: int, random_state: int):
     rng = np.random.default_rng(random_state)
     ids_random = rng.choice(y.size, n_samples_to_select, replace=False)
-    pruned_lr = LogisticRegression(max_iter=110).fit(X[ids_random], y[ids_random])
+    pruned_lr = LogisticRegression(max_iter=1000).fit(X[ids_random], y[ids_random])
     return pruned_lr.coef_, pruned_lr.intercept_
 
 
@@ -72,9 +72,9 @@ def _fastcan_pruning(
     ).fit(X)
     atoms = kmeans.cluster_centers_
     ids_fastcan = minibatch(
-        X.T, atoms.T, n_samples_to_select, batch_size=batch_size, tol=1e-9, verbose=0
+        X.T, atoms.T, n_samples_to_select, batch_size=batch_size, verbose=0
     )
-    pruned_lr = LogisticRegression(max_iter=110).fit(X[ids_fastcan], y[ids_fastcan])
+    pruned_lr = LogisticRegression(max_iter=1000).fit(X[ids_fastcan], y[ids_fastcan])
     print(atoms[-1], ids_fastcan[-10:])
     return pruned_lr.coef_, pruned_lr.intercept_
 
@@ -112,4 +112,4 @@ def plot_box(X, y, baseline, n_samples_to_select: int, n_random: int):
     plt.show()
 
 
-plot_box(data, labels, baseline_lr, n_samples_to_select=100, n_random=10)
+plot_box(data, labels, baseline_lr, n_samples_to_select=100, n_random=100)