diff --git a/python/cuml/cuml/linear_model/logistic_regression.pyx b/python/cuml/cuml/linear_model/logistic_regression.pyx
index 164821a5bd..90b275fc8e 100644
--- a/python/cuml/cuml/linear_model/logistic_regression.pyx
+++ b/python/cuml/cuml/linear_model/logistic_regression.pyx
@@ -211,61 +211,20 @@ class LogisticRegression(UniversalBase,
                          verbose=verbose,
                          output_type=output_type)
 
-        if penalty not in supported_penalties:
-            raise ValueError("`penalty` " + str(penalty) + " not supported.")
-
-        if solver not in supported_solvers:
-            raise ValueError("Only quasi-newton `qn` solver is "
-                             " supported, not %s" % solver)
         self.solver = solver
-
         self.C = C
-
-        if penalty == "none":
-            warnings.warn(
-                "The 'none' option was deprecated in version 24.06, and will "
-                "be removed in 25.08. Use None instead.",
-                FutureWarning
-            )
-            penalty = None
         self.penalty = penalty
-
         self.tol = tol
         self.fit_intercept = fit_intercept
         self.max_iter = max_iter
         self.linesearch_max_iter = linesearch_max_iter
-        self.l1_ratio = None
-        if self.penalty == "elasticnet":
-            if l1_ratio is None:
-                raise ValueError(
-                    "l1_ratio has to be specified for" "loss='elasticnet'"
-                )
-            if l1_ratio < 0.0 or l1_ratio > 1.0:
-                msg = "l1_ratio value has to be between 0.0 and 1.0"
-                raise ValueError(msg.format(l1_ratio))
-            self.l1_ratio = l1_ratio
-
-        l1_strength, l2_strength = self._get_qn_params()
-
-        loss = "sigmoid"
+        self.l1_ratio = l1_ratio
 
         if class_weight is not None:
             self._build_class_weights(class_weight)
         else:
             self.class_weight = None
 
-        self.solver_model = QN(
-            loss=loss,
-            fit_intercept=self.fit_intercept,
-            l1_strength=l1_strength,
-            l2_strength=l2_strength,
-            max_iter=self.max_iter,
-            linesearch_max_iter=self.linesearch_max_iter,
-            tol=self.tol,
-            verbose=self.verbose,
-            handle=self.handle,
-        )
-
         if logger.should_log_for(logger.level_debug):
             self.verb_prefix = "CY::"
             logger.debug(self.verb_prefix + "Estimator parameters:")
@@ -273,6 +232,33 @@ class LogisticRegression(UniversalBase,
         else:
             self.verb_prefix = ""
 
+    def _validate_params(self):
+        if self.penalty not in supported_penalties:
+            raise ValueError("`penalty` " + str(self.penalty) + " not supported.")
+
+        if self.solver not in supported_solvers:
+            raise ValueError("Only quasi-newton `qn` solver is "
+                             " supported, not %s" % self.solver)
+
+        if self.penalty == "none":
+            warnings.warn(
+                "The 'none' option was deprecated in version 24.06, and will "
+                "be removed in 25.08. Use None instead.",
+                FutureWarning
+            )
+            penalty = None
+        else:
+            penalty = self.penalty
+
+        if penalty == "elasticnet":
+            if self.l1_ratio is None:
+                raise ValueError(
+                    "l1_ratio has to be specified for" "loss='elasticnet'"
+                )
+            if self.l1_ratio < 0.0 or self.l1_ratio > 1.0:
+                msg = "l1_ratio value has to be between 0.0 and 1.0"
+                raise ValueError(msg.format(self.l1_ratio))
+
     @generate_docstring(X='dense_sparse')
     @cuml.internals.api_base_return_any(set_output_dtype=True)
     @enable_device_interop
@@ -282,6 +268,22 @@ class LogisticRegression(UniversalBase,
         Fit the model with X and y.
 
         """
+        self._validate_params()
+
+        l1_strength, l2_strength = self._get_qn_params()
+        self.solver_model = QN(
+            loss="sigmoid",
+            fit_intercept=self.fit_intercept,
+            l1_strength=l1_strength,
+            l2_strength=l2_strength,
+            max_iter=self.max_iter,
+            linesearch_max_iter=self.linesearch_max_iter,
+            tol=self.tol,
+            verbose=self.verbose,
+            handle=self.handle,
+            output_type=self.output_type,
+        )
+
         self.n_features_in_ = X.shape[1] if X.ndim == 2 else 1
         if hasattr(X, 'index'):
             self.feature_names_in_ = X.index