diff --git a/src/bayesvalidrox/surrogate_models/bayes_linear.py b/src/bayesvalidrox/surrogate_models/bayes_linear.py
index a8aeb54d0189dafbe445900fa2e8e10df78a0125..8fee8bb429cfbc27c9b57156bf09fc6db46e2414 100644
--- a/src/bayesvalidrox/surrogate_models/bayes_linear.py
+++ b/src/bayesvalidrox/surrogate_models/bayes_linear.py
@@ -266,7 +266,7 @@ class EBLinearRegression(BayesianLinearRegression):
             converged = self._check_convergence(mu, mu_old)
             if self.verbose:
                 print(f"Iteration {i} completed")
-                if converged is True:
+                if converged == True:
                     print(f"Algorithm converged after {i} iterations")
             if converged or i == self.n_iter - 1:
                 break
@@ -470,9 +470,9 @@ class VBLinearRegression(BayesianLinearRegression):
 
             # check convergence
             converged = self._check_convergence(mu, mu_old)
-            if self.verbose is True:
+            if self.verbose == True:
                 print(f"Iteration {i} is completed")
-                if converged is True:
+                if converged == True:
                     print(f"Algorithm converged after {i} iterations")
 
             # terminate if convergence or maximum number of iterations are achieved
diff --git a/src/bayesvalidrox/surrogate_models/reg_fast_ard.py b/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
index 9117b5bee61f3bc457454d50630039653006a565..2a8aa1163a344b93291f1e743f541b51bb06b8bd 100755
--- a/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
+++ b/src/bayesvalidrox/surrogate_models/reg_fast_ard.py
@@ -24,8 +24,8 @@ def update_precisions(Q, S, q, s, A, active, tol, n_samples, clf_bias):
 
     # identify features that can be added , recomputed and deleted in model
     theta = q**2 - s
-    add = (theta > 0) * (active is False)
-    recompute = (theta > 0) * (active is True)
+    add = (theta > 0) * (active==False)
+    recompute = (theta > 0) * (active==True)
     delete = ~(add + recompute)
 
     # compute sparsity & quality parameters corresponding to features in
@@ -66,11 +66,11 @@ def update_precisions(Q, S, q, s, A, active, tol, n_samples, clf_bias):
     # if not converged update precision parameter of weights and return
     if theta[feature_index] > 0:
         A[feature_index] = s[feature_index]**2 / theta[feature_index]
-        if active[feature_index] is False:
+        if active[feature_index]==False:
             active[feature_index] = True
     else:
         # at least two active features
-        if active[feature_index] is True and np.sum(active) >= 2:
+        if active[feature_index]==True and np.sum(active) >= 2:
             # do not remove bias term in classification
             # (in regression it is factored in through centering)
             if not (feature_index == 0 and clf_bias):