diff --git a/python/naiveautoml/algorithm_selection/_sklearn_factory.py b/python/naiveautoml/algorithm_selection/_sklearn_factory.py
index b01a5cf..afac376 100644
--- a/python/naiveautoml/algorithm_selection/_sklearn_factory.py
+++ b/python/naiveautoml/algorithm_selection/_sklearn_factory.py
@@ -662,6 +662,10 @@ def score_func(X, y):
     if clazz == sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis:
         return sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(**params)
 
+    if clazz == sklearn.linear_model.LogisticRegression:
+        params["dual"] = check_for_bool(params["dual"])
+        return sklearn.linear_model.LogisticRegression(**params)
+
     if clazz == sklearn.neural_network.MLPClassifier:
         max_iter = 512  # hard coded in auto-sklearn
         hidden_layer_depth = int(params["hidden_layer_depth"])
diff --git a/python/naiveautoml/searchspace-classification.json b/python/naiveautoml/searchspace-classification.json
index bcced3b..88364c7 100644
--- a/python/naiveautoml/searchspace-classification.json
+++ b/python/naiveautoml/searchspace-classification.json
@@ -1300,6 +1300,43 @@
                     "json_format_version": 0.2
                 }
             },
+            {
+                "class": "sklearn.linear_model.LogisticRegression",
+                "params": {
+                    "hyperparameters": [
+                        {
+                            "name": "penalty",
+                            "type": "categorical",
+                            "choices": [
+                                "l1",
+                                "l2",
+                                "elasticnet",
+                                "None"
+                            ],
+                            "default": "l2"
+                        },
+                        {
+                            "name": "dual",
+                            "type": "categorical",
+                            "choices": [
+                                "True",
+                                "False"
+                            ],
+                            "default": "False"
+                        },
+                        {
+                            "name": "C",
+                            "type": "uniform_float",
+                            "log": true,
+                            "lower": 0.03125,
+                            "upper": 32768.0,
+                            "default": 1.0
+                        }
+                    ],
+                    "conditions": [],
+                    "forbiddens": []
+                }
+            },
             {
                 "class": "sklearn.neural_network._multilayer_perceptron.MLPClassifier",
                 "params": {
diff --git a/python/naiveautoml/searchspace-regression.json b/python/naiveautoml/searchspace-regression.json
index db648be..9f29b7b 100644
--- a/python/naiveautoml/searchspace-regression.json
+++ b/python/naiveautoml/searchspace-regression.json
@@ -941,7 +941,7 @@
                             "name": "alpha",
                             "type": "uniform_float",
                             "log": true,
-                            "lower": 1e-14,
+                            "lower": 1e-10,
                             "upper": 1.0,
                             "default": 1e-08
                         },
diff --git a/python/requirements.txt b/python/requirements.txt
index 5c6b274..232ce09 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -1,7 +1,7 @@
 numpy<2.0.0
 pandas
 scikit-learn>=1.3
-configspace<0.7.1
+configspace==1.2
 scipy
 pynisher
 psutil
diff --git a/python/test/test_naiveautoml.py b/python/test/test_naiveautoml.py
index 197e3a2..50306e6 100644
--- a/python/test/test_naiveautoml.py
+++ b/python/test/test_naiveautoml.py
@@ -298,7 +298,7 @@ def test_constant_algorithms_in_hpo_phase(self):
             field = f"{step}_class"
             class_in_phase1 = best_solution_in_phase_1[field]
             class_in_phase2 = pd.unique(history[field])[0]
-            self.assertEquals(
+            self.assertEqual(
                 class_in_phase1,
                 class_in_phase2,
                 f"Choice for {step} should conicide but is {class_in_phase1} in AS phase and {class_in_phase2} in HPO."