diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py
index 53607ad..3deae61 100644
--- a/exploration/ml_pipeline_classification.py
+++ b/exploration/ml_pipeline_classification.py
@@ -36,6 +36,8 @@ nb_dir = os.path.split(os.getcwd())[0]
 if nb_dir not in sys.path:
     sys.path.append(nb_dir)
     
+import machine_learning.helper
+
 # %% [markdown]
 # # RAPIDS models
 
@@ -70,14 +72,20 @@ model_input['target'].value_counts()
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
 # UnderSampling
 if undersampling:
-    model_input.groupby("pid").count()
-    no_stress = model_input[model_input['target'] == 0]
-    stress = model_input[model_input['target'] == 1]
+  model_input_new = pd.DataFrame(columns=model_input.columns)
+  for pid in model_input["pid"].unique():
+    stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 1)]
+    no_stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 0)]
+    if (len(stress) == 0):
+      continue
+    if (len(no_stress) == 0):
+      continue
+    model_input_new = pd.concat([model_input_new, stress], axis=0)
     
-    no_stress = no_stress.sample(n=len(stress))
-    model_input = pd.concat([stress,no_stress], axis=0)
-    
-    model_input["target"].value_counts()
+    no_stress = no_stress.sample(n=min(len(stress), len(no_stress)))
+    # In case there are more stress samples than no_stress, take all instances of no_stress.
+    model_input_new = pd.concat([model_input_new, no_stress], axis=0)
+  model_input = model_input_new
 
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
@@ -152,6 +160,18 @@ print("F1", np.mean(dummy_classifier['test_f1']))
 print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
 print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl]))
 
+# %% [markdown] nteract={"transient": {"deleting": false}}
+# ### All models
+
+# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
+final_scores = machine_learning.helper.run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
+
+# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
+# %%
+final_scores.index.name = "metric"
+final_scores = final_scores.set_index(["method", final_scores.index])
+final_scores.to_csv("../presentation/event_stressful_detection_5fold.csv")
+
 # %% [markdown]
 # ### Logistic Regression