diff --git a/README.md b/README.md
index f7551551..04920a14 100644
--- a/README.md
+++ b/README.md
@@ -38,6 +38,34 @@ conda create --name tpot2env python=3.10
 conda activate tpot2env
 ```
 
+### Packages Used
+
+python version <3.12
+numpy
+scipy
+scikit-learn
+update_checker
+tqdm
+stopit
+pandas
+joblib
+xgboost
+matplotlib
+traitlets
+lightgbm
+optuna
+baikal
+jupyter
+networkx>
+dask
+distributed
+dask-ml
+dask-jobqueue
+func_timeout
+configspace
+
+Many of the hyperparameter ranges used in our configspaces were adapted from either the original TPOT package or the AutoSklearn package. 
+
 ### Note for M1 Mac or other Arm-based CPU users
 
 You need to install the lightgbm package directly from conda using the following command before installing TPOT2. 
diff --git a/tpot2/config/classifiers.py b/tpot2/config/classifiers.py
index 6423f328..4be33797 100644
--- a/tpot2/config/classifiers.py
+++ b/tpot2/config/classifiers.py
@@ -292,8 +292,8 @@ def get_PassiveAggressiveClassifier_ConfigurationSpace(random_state):
 #TODO support auto shrinkage when solver is svd. may require custom node
 def get_LinearDiscriminantAnalysis_ConfigurationSpace():
 
-    solver = Categorical("solver", ['svd', 'lsqr', 'eigen']),
-    shrinkage = Float("shrinkage", bounds=(0, 1)),
+    solver = Categorical("solver", ['svd', 'lsqr', 'eigen'])
+    shrinkage = Float("shrinkage", bounds=(0, 1))
 
     shrinkcond = NotEqualsCondition(shrinkage, solver, 'svd')
 
@@ -301,7 +301,7 @@ def get_LinearDiscriminantAnalysis_ConfigurationSpace():
     cs.add_hyperparameters([solver, shrinkage])
     cs.add_conditions([shrinkcond])
 
-    return 
+    return cs
 
 
 
diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py
index 473233ea..19dfb531 100644
--- a/tpot2/config/get_configspace.py
+++ b/tpot2/config/get_configspace.py
@@ -192,6 +192,8 @@ def get_configspace(name, n_classes=3, n_samples=100, n_features=100, random_sta
 
 
         #classifiers.py
+        case "LinearDiscriminantAnalysis":
+            return classifiers.get_LinearDiscriminantAnalysis_ConfigurationSpace()
         case "AdaBoostClassifier":
             return classifiers.get_AdaBoostClassifier_ConfigurationSpace(random_state=random_state)
         case "LogisticRegression":
@@ -232,6 +234,10 @@ def get_configspace(name, n_classes=3, n_samples=100, n_features=100, random_sta
             return regressors.ElasticNetCV_configspace
         case "RidgeCV":
             return {}
+        case "PassiveAggressiveClassifier":
+            return classifiers.get_PassiveAggressiveClassifier_ConfigurationSpace(random_state=random_state)
+        case "QuadraticDiscriminantAnalysis":
+            return classifiers.get_QuadraticDiscriminantAnalysis_ConfigurationSpace()
 
         #regressors.py
         case "RandomForestRegressor":
diff --git a/tpot2/tests/test_estimators.py b/tpot2/tests/test_estimators.py
index 98b607e0..f1f3d45a 100644
--- a/tpot2/tests/test_estimators.py
+++ b/tpot2/tests/test_estimators.py
@@ -4,11 +4,29 @@
 import random
 import sklearn
 
+@pytest.fixture
+def sample_dataset():
+    X_train, y_train = load_iris(return_X_y=True)
+    return X_train, y_train
+
 #standard test
 @pytest.fixture
 def tpot_estimator():
-    return tpot2.TPOTEstimator(  population_size=10,
-                            generations=5,
+
+    n_classes=3
+    n_samples=100
+    n_features=100
+
+    search_space = tpot2.search_spaces.pipelines.GraphPipeline(
+            root_search_space= tpot2.config.get_search_space("classifiers", n_samples=n_samples, n_features=n_features, n_classes=n_classes),
+            leaf_search_space = None, 
+            inner_search_space = tpot2.config.get_search_space(["selectors","transformers","classifiers"],n_samples=n_samples, n_features=n_features, n_classes=n_classes),
+            max_size = 10,
+        )
+    return tpot2.TPOTEstimator(  
+                            search_space=search_space,
+                            population_size=10,
+                            generations=2,
                             scorers=['roc_auc_ovr'],
                             scorers_weights=[1],
                             classification=True,
@@ -16,13 +34,18 @@ def tpot_estimator():
                             early_stop=5,
                             other_objective_functions= [],
                             other_objective_functions_weights=[],
-                            max_time_seconds=300,
+                            max_time_seconds=30,
                             verbose=3)
 
 @pytest.fixture
-def sample_dataset():
-    X_train, y_train = load_iris(return_X_y=True)
-    return X_train, y_train
+def tpot_classifier():
+    return tpot2.tpot_estimator.templates.TPOTClassifier(max_time_seconds=10,verbose=3)
+
+@pytest.fixture
+def tpot_regressor():
+    return tpot2.tpot_estimator.templates.TPOTRegressor(max_time_seconds=10,verbose=3)
+
+
 
 def test_tpot_estimator_fit(tpot_estimator,sample_dataset):
     #load iris dataset
@@ -80,13 +103,7 @@ def test_tpot_estimator_config_dict_type():
 
 
 
-@pytest.fixture
-def tpot_classifier():
-    return tpot2.tpot_estimator.templates.TPOTClassifier(max_time_seconds=10,verbose=3)
 
-@pytest.fixture
-def tpot_regressor():
-    return tpot2.tpot_estimator.templates.TPOTRegressor(max_time_seconds=10,verbose=3)
 
 def test_tpot_classifier_fit(tpot_classifier,sample_dataset):
     #load iris dataset