diff --git a/.cookietemple.yml b/.cookietemple.yml
index 2f371d50..6a175245 100644
--- a/.cookietemple.yml
+++ b/.cookietemple.yml
@@ -15,5 +15,5 @@ full_name: Lukas Heumos
 email: lukas.heumos@posteo.net
 project_name: spapros
 project_short_description: Fully automated spatial transcriptomics probe selection.
-version: 0.1.2
+version: 0.1.3
 license: MIT
diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml
index 9bd97a98..0fdc078b 100644
--- a/.github/release-drafter.yml
+++ b/.github/release-drafter.yml
@@ -1,5 +1,5 @@
-name-template: "0.1.2 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
-tag-template: 0.1.2 # <<COOKIETEMPLE_FORCE_BUMP>>
+name-template: "0.1.3 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
+tag-template: 0.1.3 # <<COOKIETEMPLE_FORCE_BUMP>>
 categories:
     - title: "🚀 Features"
       labels:
diff --git a/cookietemple.cfg b/cookietemple.cfg
index c80df996..0defe163 100644
--- a/cookietemple.cfg
+++ b/cookietemple.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.2
+current_version = 0.1.3
 
 [bumpversion_files_whitelisted]
 init_file = spapros/__init__.py
diff --git a/docs/conf.py b/docs/conf.py
index a6a14461..a3c67023 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -102,9 +102,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = "0.1.2"
+version = "0.1.3"
 # The full version, including alpha/beta/rc tags.
-release = "0.1.2"
+release = "0.1.3"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/pyproject.toml b/pyproject.toml
index 81079d66..a7cb6bc8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "spapros"
-version = "0.1.2"  # <<COOKIETEMPLE_FORCE_BUMP>>
+version = "0.1.3"  # <<COOKIETEMPLE_FORCE_BUMP>>
 description = "Probe set selection for targeted spatial transcriptomics."
 authors = ["Lukas Heumos <lukas.heumos@posteo.net>", "Louis Kümmerle <louis.kuemmerle@helmholtz-muenchen.de>"]
 license = "MIT"
diff --git a/spapros/__init__.py b/spapros/__init__.py
index c9a384c8..2fa35177 100644
--- a/spapros/__init__.py
+++ b/spapros/__init__.py
@@ -2,7 +2,7 @@
 
 __author__ = "Lukas Heumos"
 __email__ = "lukas.heumos@posteo.net"
-__version__ = "0.1.2"
+__version__ = "0.1.3"
 
 __all__ = ["se", "ev", "pl", "ut"]
 
diff --git a/spapros/evaluation/evaluation.py b/spapros/evaluation/evaluation.py
index d6771c37..4d89f05e 100644
--- a/spapros/evaluation/evaluation.py
+++ b/spapros/evaluation/evaluation.py
@@ -1,4 +1,5 @@
 import os
+import gc
 import pickle
 import warnings
 from enum import Enum
@@ -1912,6 +1913,11 @@ def single_forest_classifications(
             ct_trees[ct].append(ct_trees_i[ct])
         if progress and verbose:
             progress.advance(forest_task)
+        # garbage collection
+        del X_train
+        del y_train
+        del cts_train
+        gc.collect()
     # Get feature importances
     importances = {
         ct: pd.DataFrame(index=a.var.index, columns=[str(i) for i in range(n_trees)], dtype="float64")
@@ -1939,6 +1945,11 @@ def single_forest_classifications(
         cts_test=cts_test,
         masks=masks_test,
     )
+    #garbage collection
+    del X_test
+    del y_test
+    del cts_test
+    gc.collect()    
 
     # Sort results
     if sort_by_tree_performance:
diff --git a/spapros/evaluation/metrics.py b/spapros/evaluation/metrics.py
index 3357234f..12dd944e 100644
--- a/spapros/evaluation/metrics.py
+++ b/spapros/evaluation/metrics.py
@@ -1000,18 +1000,17 @@ def xgboost_forest_classification(
             n_classes = len(np.unique(train_y))
             clf = XGBClassifier(
                 max_depth=max_depth,
-                num_class=n_classes,
+                num_class=n_classes if n_classes > 2 else None,
                 n_estimators=250,
                 objective="multi:softmax" if n_classes > 2 else "binary:logistic",
                 early_stopping_rounds=5,
-                eval_metric="mlogloss",
+                eval_metric="mlogloss" if n_classes > 2 else "logloss",
                 learning_rate=lr,
                 colsample_bytree=colsample_bytree,
                 min_child_weight=min_child_weight,
                 gamma=gamma,
                 booster="gbtree",  # TODO: compare with 'dart',rate_drop= 0.1
                 random_state=seed,
-                use_label_encoder=False,  # To get rid of deprecation warning we convert labels into ints
                 n_jobs=n_jobs,
             )
             clf.fit(