diff --git a/imodels/algebraic/gam_multitask.py b/imodels/algebraic/gam_multitask.py
index 04c1d297..038006b9 100644
--- a/imodels/algebraic/gam_multitask.py
+++ b/imodels/algebraic/gam_multitask.py
@@ -31,7 +31,7 @@ class MultiTaskGAM(BaseEstimator):
 
     def __init__(
         self,
-        ebm_kwargs=None,
+        ebm_kwargs={},
         multitask=True,
         random_state=42,
 
@@ -43,11 +43,13 @@ def __init__(
         self.ebm_kwargs = ebm_kwargs
         self.multitask = multitask
         self.random_state = random_state
+        if not 'random_state' in ebm_kwargs:
+            ebm_kwargs['random_state'] = random_state
         self.ebm_ = ExplainableBoostingRegressor(**(ebm_kwargs or {}))
 
         # self.ebm_ = ExplainableBoostingClassifier(**(ebm_kwargs or {}))
 
-    def fit(self, X, y, task_weights=None, sample_weight=None):
+    def fit(self, X, y, sample_weight=None):
         X, y = check_X_y(X, y, accept_sparse=False, multi_output=False)
         if isinstance(self, ClassifierMixin):
             check_classification_targets(y)
@@ -64,7 +66,7 @@ def fit(self, X, y, task_weights=None, sample_weight=None):
         num_features = X.shape[1]
         for task_num in tqdm(range(num_features)):
             self.ebms_[task_num] = deepcopy(self.ebm_)
-            y_ = X[:, task_num]
+            y_ = np.ascontiguousarray(X[:, task_num])
             X_ = deepcopy(X)
             X_[:, task_num] = 0
             self.ebms_[task_num].fit(X_, y_, sample_weight=sample_weight)
@@ -77,7 +79,7 @@ def fit(self, X, y, task_weights=None, sample_weight=None):
         feats = self.extract_ebm_features(X)
 
         # fit a linear model to the features
-        self.lin_model = RidgeCV()
+        self.lin_model = RidgeCV(alphas=np.logspace(-2, 3, 7))
         self.lin_model.fit(feats, y)
         return self
 
@@ -124,7 +126,8 @@ class MultiTaskGAMClassifier(MultiTaskGAM, ClassifierMixin):
 
 
 if __name__ == "__main__":
-    X, y, feature_names = imodels.get_clean_dataset("heart")
+    # X, y, feature_names = imodels.get_clean_dataset("heart")
+    X, y, feature_names = imodels.get_clean_dataset("bike_sharing")
     # X, y, feature_names = imodels.get_clean_dataset("diabetes")
 
     # remove some features to speed things up
@@ -135,13 +138,14 @@ class MultiTaskGAMClassifier(MultiTaskGAM, ClassifierMixin):
         random_state=42,
     )
     results = defaultdict(list)
-    for gam in [
-            MultiTaskGAMRegressor(multitask=True),
+    for gam in tqdm([
             MultiTaskGAMRegressor(multitask=False),
-    ]:
+            MultiTaskGAMRegressor(multitask=True),
+    ]):
         np.random.seed(42)
-        gam.fit(X, y_train)
         results["model_name"].append(gam)
+        print('Fitting', results['model_name'][-1])
+        gam.fit(X, y_train)
 
         # check roc auc score
         # y_pred = gam.predict_proba(X_test)[:, 1]
diff --git a/imodels/util/data_util.py b/imodels/util/data_util.py
index e6af6c83..83b6f8cf 100644
--- a/imodels/util/data_util.py
+++ b/imodels/util/data_util.py
@@ -15,7 +15,7 @@
 
 DSET_CLASSIFICATION_KWARGS = {
     # classification
-    "pima_diabetes": {"dataset_name": "40715", "data_source": "openml"},
+    "pima_diabetes": {"dataset_name": 40715, "data_source": "openml"},
     "sonar": {"dataset_name": "sonar", "data_source": "pmlb"},
     "heart": {"dataset_name": "heart", "data_source": "imodels"},
     "diabetes": {"dataset_name": "diabetes", "data_source": "pmlb"},
@@ -36,7 +36,7 @@
         "data_source": "imodels",
     },  # big, 100k points
     # big, 1e6 points
-    "adult": {"dataset_name": "1182", "data_source": "openml"},
+    "adult": {"dataset_name": 1182, "data_source": "openml"},
     # CDI classification
     "csi_pecarn": {"dataset_name": "csi_pecarn_pred", "data_source": "imodels"},
     "iai_pecarn": {"dataset_name": "iai_pecarn_pred", "data_source": "imodels"},
@@ -45,12 +45,12 @@
 
 DSET_REGRESSION_KWARGS = {
     # regression
-    "bike_sharing": {"dataset_name": "42712", "data_source": "openml"},
+    "bike_sharing": {"dataset_name": 42712, "data_source": "openml"},
     "friedman1": {"dataset_name": "friedman1", "data_source": "synthetic"},
     "friedman2": {"dataset_name": "friedman2", "data_source": "synthetic"},
     "friedman3": {"dataset_name": "friedman3", "data_source": "synthetic"},
     "diabetes_regr": {"dataset_name": "diabetes", "data_source": "sklearn"},
-    "abalone": {"dataset_name": "183", "data_source": "openml"},
+    "abalone": {"dataset_name": 183, "data_source": "openml"},
     "echo_months": {"dataset_name": "1199_BNG_echoMonths", "data_source": "pmlb"},
     "satellite_image": {"dataset_name": "294_satellite_image", "data_source": "pmlb"},
     "california_housing": {