diff --git a/pyprophet/scoring/classifiers.py b/pyprophet/scoring/classifiers.py
index 9daac76..f8f91fe 100644
--- a/pyprophet/scoring/classifiers.py
+++ b/pyprophet/scoring/classifiers.py
@@ -27,6 +27,7 @@
     train_test_split,
 )
 from sklearn.svm import LinearSVC
+from sklearn.ensemble import HistGradientBoostingClassifier
 
 from .data_handling import Experiment
 
@@ -299,6 +300,41 @@ def set_parameters(self, classifier):
         return self
 
 
+class HistGBCLearner(AbstractLearner):
+    def __init__(self, autotune=False, threads=1):
+        self.classifier = None
+        self.importance = None
+        self.autotune = autotune
+        self.threads = threads
+
+    def tune(
+        self, decoy_peaks, target_peaks, use_main_score=True, cv_splits=3, n_jobs=-1
+    ):
+        raise NotImplementedError(
+            "Hyperparameter tuning for HistGradientBoostingClassifier is not implemented."
+        )
+
+    def learn(self, decoy_peaks, target_peaks, use_main_score=True):
+        assert isinstance(decoy_peaks, Experiment)
+        assert isinstance(target_peaks, Experiment)
+
+        X0 = decoy_peaks.get_feature_matrix(use_main_score)
+        X1 = target_peaks.get_feature_matrix(use_main_score)
+        X = np.vstack((X0, X1))
+        y = np.zeros((X.shape[0],))
+        y[X0.shape[0] :] = 1.0
+
+        classifier = HistGradientBoostingClassifier(
+            random_state=42, max_iter=100, early_stopping=True, validation_fraction=0.1
+        )
+        classifier.fit(X, y)
+
+        self.classifier = classifier
+        # self.importance = classifier.feature_importances_
+
+        return self
+
+
 class XGBLearner(AbstractLearner):
     """
     Implements an XGBoost-based learner for scoring.