diff --git a/scorers/classification/binary/cost_smooth.py b/scorers/classification/binary/cost_smooth.py
new file mode 100644
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+++ b/scorers/classification/binary/cost_smooth.py
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+"""Using hard-coded dollar amounts x for false positives and y for false negatives, calculate the cost of a model using: `(1 - y_true) * y_pred * fp_cost + y_true * (1 - y_pred) * fn_cost`"""
+
+import typing
+import numpy as np
+from h2oaicore.metrics import CustomScorer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import confusion_matrix
+
+
+class CostBinary_smooth(CustomScorer):
+    _description = "Calculates cost per row in binary classification: `(1 - y_true) * y_pred * fp_cost + y_true * (1 - y_pred) * fn_cost`"
+    _binary = True
+    _maximize = False
+    _perfect_score = 0
+    _display_name = "Cost_smooth"
+
+    # The cost of false positives and negatives will vary by data set, we use the rules from the below as an example
+    # https://www.kaggle.com/uciml/aps-failure-at-scania-trucks-data-set
+    _fp_cost = 75
+    _fn_cost = 70
+
+    def score(self,
+              actual: np.array,
+              predicted: np.array,
+              sample_weight: typing.Optional[np.array] = None,
+              labels: typing.Optional[np.array] = None) -> float:
+        
+        lb = LabelEncoder()
+        labels = list(lb.fit_transform(labels))
+        actual = lb.transform(actual)
+        
+        if sample_weight is None:
+            sample_weight = np.ones(actual.shape[0])
+
+        return np.sum(((1-actual) * predicted * self.__class__._fp_cost + actual * (1-predicted) * self.__class__._fn_cost)*sample_weight)/np.sum(sample_weight)