diff --git a/dataset_eval/evaluate.py b/dataset_eval/evaluate.py
index ecadbbfbd..0a6326a8b 100644
--- a/dataset_eval/evaluate.py
+++ b/dataset_eval/evaluate.py
@@ -60,28 +60,8 @@ def evaluate_dataset(eval_job, dataset_dir, storage_dir):
         with open(groundtruth_path, "w") as f:
             yaml.dump(create_groundtruth_dict(snapshot["data"]["name"], train), f)
 
-        # logging.info("Training GAIA model...")
-        # evaluate_gaia(eval_location, groundtruth_path, filelist_path, storage_dir, eval_job)
-
-        # Passing more user preferences to train the model.
-        logging.info("Training model...")
-        results = gaia_wrapper.train_model(
-            project_dir=eval_location,
-            groundtruth_file=groundtruth_path,
-            filelist_file=filelist_path,
-            c_values=eval_job["options"].get("c_values", []),
-            gamma_values=eval_job["options"].get("gamma_values", []),
-            preprocessing_values=eval_job["options"].get("preprocessing_values", []),
-        )
-        logging.info("Saving results...")
-        save_history_file(storage_dir, results["history_path"], eval_job["id"])
-        db.dataset_eval.set_job_result(eval_job["id"], json.dumps({
-            "project_path": eval_location,
-            "parameters": results["parameters"],
-            "accuracy": results["accuracy"],
-            "confusion_matrix": results["confusion_matrix"],
-            "history_path": results["history_path"],
-        }))
+        logging.info("Training GAIA model...")
+        evaluate_gaia(eval_location, groundtruth_path, filelist_path, storage_dir, eval_job)
 
         db.dataset_eval.set_job_status(eval_job["id"], db.dataset_eval.STATUS_DONE)
         logging.info("Evaluation job %s has been completed." % eval_job["id"])
@@ -104,6 +84,7 @@ def evaluate_dataset(eval_job, dataset_dir, storage_dir):
 
 
 def evaluate_gaia(eval_location, groundtruth_path, filelist_path, storage_dir, eval_job):
+    # Passing more user preferences to train the model.
     results = gaia_wrapper.train_model(
         project_dir=eval_location,
         groundtruth_file=groundtruth_path,
@@ -123,9 +104,11 @@ def evaluate_gaia(eval_location, groundtruth_path, filelist_path, storage_dir, e
     }))
 
 
-def evaluate_sklearn(eval_location, groundtruth_path, filelist_path, storage_dir, eval_job):
-    # create_classification_project(ground_truth_directory=groundtruth_path)
-    pass
+def evaluate_sklearn(eval_location, dataset_dir, storage_dir, eval_job):
+    create_classification_project(ground_truth_directory=dataset_dir,
+                                  project_file=eval_job["id"],
+                                  exports_directory=eval_job["id"],
+                                  exports_path=eval_location)
 
 
 def create_groundtruth_dict(name, datadict):