diff --git a/src/redflag/distributions.py b/src/redflag/distributions.py
index ec362b2..f406304 100644
--- a/src/redflag/distributions.py
+++ b/src/redflag/distributions.py
@@ -394,8 +394,8 @@ def fit_kde(a: ArrayLike, bandwidth: float=1.0, kernel: str='gaussian') -> tuple
         >>> rng = np.random.default_rng(42)
         >>> data = rng.normal(size=100)
         >>> x, kde = fit_kde(data)
-        >>> x[0]
-        -3.2124714013056916
+        >>> x[0] + 3.2124714013056916 < 1e-9
+        True
         >>> kde[0] - 0.014367259502733645 < 1e-9
         True
         >>> len(kde)
@@ -433,8 +433,8 @@ def get_kde(a: ArrayLike, method: str='scott') -> tuple[np.ndarray, np.ndarray]:
         >>> rng = np.random.default_rng(42)
         >>> data = rng.normal(size=100)
         >>> x, kde = get_kde(data)
-        >>> x[0]
-        -1.354649738246933
+        >>> x[0] + 1.354649738246933 < 1e-9
+        True
         >>> kde[0] - 0.162332012191087 < 1e-9
         True
         >>> len(kde)
diff --git a/src/redflag/importance.py b/src/redflag/importance.py
index adb7395..45f4171 100644
--- a/src/redflag/importance.py
+++ b/src/redflag/importance.py
@@ -84,17 +84,17 @@ def feature_importances(X: ArrayLike, y: ArrayLike=None,
     # Train three models and gather the importances.
     imps: list = []
     if task == 'classification':
-        imps.append(np.abs(LogisticRegression().fit(X, y).coef_.sum(axis=0)))
+        imps.append(np.abs(LogisticRegression(random_state=random_state).fit(X, y).coef_.sum(axis=0)))
         imps.append(RandomForestClassifier(random_state=random_state).fit(X, y).feature_importances_)
         model = KNeighborsClassifier().fit(X_train, y_train)
-        r = permutation_importance(model, X_val, y_val, n_repeats=10, scoring='f1_weighted', random_state=random_state)
+        r = permutation_importance(model, X_val, y_val, n_repeats=8, scoring='f1_weighted', random_state=random_state)
         imps.append(r.importances_mean)
     elif task == 'regression':
         # Need data to be scaled, but don't necessarily want to scale entire dataset.
-        imps.append(np.abs(Lasso().fit(X, y).coef_))
+        imps.append(np.abs(Lasso(random_state=random_state).fit(X, y).coef_))
         imps.append(RandomForestRegressor(random_state=random_state).fit(X, y).feature_importances_)
         model = KNeighborsRegressor().fit(X_train, y_train)
-        r = permutation_importance(model, X_val, y_val, n_repeats=10, scoring='neg_mean_squared_error', random_state=random_state)
+        r = permutation_importance(model, X_val, y_val, n_repeats=8, scoring='neg_mean_squared_error', random_state=random_state)
         if not all(r.importances_mean < 0):
             r.importances_mean[r.importances_mean < 0] = 1e-9
             imps.append(r.importances_mean)
diff --git a/src/redflag/utils.py b/src/redflag/utils.py
index 6dcec1b..cc403f9 100644
--- a/src/redflag/utils.py
+++ b/src/redflag/utils.py
@@ -370,7 +370,7 @@ def is_standard_normal(a: ArrayLike, confidence: float=0.95) -> bool:
         bool: True if the feature appears to have a standard normal distribution.
 
     Example:
-        >>> a = np.random.normal(size=1000)
+        >>> a = np.random.normal(size=2000)
         >>> is_standard_normal(a, confidence=0.9)
         True
         >>> is_standard_normal(a + 1)