diff --git a/BayesianKDEy/full_experiments.py b/BayesianKDEy/full_experiments.py
index 9e03062..4ec0fd6 100644
--- a/BayesianKDEy/full_experiments.py
+++ b/BayesianKDEy/full_experiments.py
@@ -54,7 +54,7 @@ def methods():
         quantifier with optimized hyperparameters
     """
     acc_hyper = {}
-    emq_hyper = {'calib': [None, 'nbvs', 'bcts', 'ts', 'vs']}
+    emq_hyper = {'calib': ['nbvs', 'bcts', 'ts', 'vs']}
     hdy_hyper = {'nbins': [3,4,5,8,16,32]}
     kdey_hyper = {'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
     kdey_hyper_clr = {'bandwidth': [0.05, 0.1, 0.5, 1., 2., 5.]}
@@ -66,7 +66,7 @@ def methods():
     # yield 'BootstrapACC', ACC(LR()), acc_hyper, lambda hyper: AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), multiclass_method
     # yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0), multiclass_method
 
-    yield 'BootstrapEMQ', EMQ(LR(), on_calib_error='backup'), emq_hyper, lambda hyper: AggregativeBootstrap(EMQ(LR(), on_calib_error='backup', calib=hyper['calib'], val_split=None if hyper['calib'] is None else 5z), n_test_samples=1000, random_state=0), multiclass_method
+    yield 'BootstrapEMQ', EMQ(LR(), on_calib_error='backup', val_split=5), emq_hyper, lambda hyper: AggregativeBootstrap(EMQ(LR(), on_calib_error='backup', calib=hyper['calib'], val_split=5), n_test_samples=1000, random_state=0), multiclass_method
 
     # yield 'BootstrapHDy', DMy(LR()), hdy_hyper, lambda hyper: AggregativeBootstrap(DMy(LR(), **hyper), n_test_samples=1000, random_state=0), multiclass_method
     # yield 'BayesianHDy', DMy(LR()), hdy_hyper, lambda hyper: PQ(LR(), stan_seed=0, **hyper), only_binary
@@ -151,7 +151,7 @@ def experiment_path(dir:Path, dataset_name:str, method_name:str):
 if __name__ == '__main__':
 
     binary = {
-        'datasets': qp.datasets.UCI_BINARY_DATASETS,
+        'datasets': qp.datasets.UCI_BINARY_DATASETS[1:],
         'fetch_fn': qp.datasets.fetch_UCIBinaryDataset,
         'sample_size': 500
     }