diff --git a/code/classification/run_classifier.py b/code/classification/run_classifier.py
index 414e0ce5..48631f73 100644
--- a/code/classification/run_classifier.py
+++ b/code/classification/run_classifier.py
@@ -10,12 +10,14 @@
 
 import argparse, pickle
 from sklearn.dummy import DummyClassifier
+
 from sklearn.metrics import accuracy_score, cohen_kappa_score
 from sklearn.preprocessing import StandardScaler
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.pipeline import make_pipeline
 from mlflow import log_metric, log_param, set_tracking_uri
 
+
 # setting up CLI
 parser = argparse.ArgumentParser(description = "Classifier")
 parser.add_argument("input_file", help = "path to the input pickle file")
@@ -26,8 +28,12 @@
 parser.add_argument("-f", "--frequency", action = "store_true", help = "label frequency classifier")
 parser.add_argument("--knn", type = int, help = "k nearest neighbor classifier with the specified value of k", default = None)
 parser.add_argument("-a", "--accuracy", action = "store_true", help = "evaluate using accuracy")
+
+
+
 parser.add_argument("-k", "--kappa", action = "store_true", help = "evaluate using Cohen's kappa")
 parser.add_argument("--log_folder", help = "where to log the mlflow results", default = "data/classification/mlflow")
+
 args = parser.parse_args()
 
 # load data
@@ -83,9 +89,17 @@
 evaluation_metrics = []
 if args.accuracy:
     evaluation_metrics.append(("accuracy", accuracy_score))
+    
+if args.area:
+    evaluation_metrics.append(("area_under_curve", roc_auc_score))
+    
+if args.cohen:
+    evaluation_metrics.append(("cohen", cohen_kappa_score))
+=======
 if args.kappa:
     evaluation_metrics.append(("Cohen_kappa", cohen_kappa_score))
 
+
 # compute and print them
 for metric_name, metric in evaluation_metrics:
     metric_value = metric(data["labels"], prediction)