--- a
+++ b/binary_classification/boosted_tree.py
@@ -0,0 +1,37 @@
+import xgboost as xgb
+from sklearn.metrics import roc_auc_score
+from util import roc_results, results
+
+
+def xgb_training(x_train, y_train):
+    """
+    :param x_train: the x-values we want to train on (2D numpy array)
+    :param y_train: the y-values that correspond to x_train (1D numpy array)
+    :return: XGBoost Classifier object that can now be used for predictions
+    """
+    clf = xgb.XGBClassifier(random_state=0)
+    clf.fit(x_train, y_train)
+    return clf
+
+
+def xgb_classification(clf, x_test):
+    """
+    :param clf: trained XGBoost Classifier object
+    :param x_test: the x-values we want to get predictions on (2D numpy array)
+    :return: a 1D numpy array containing the predictions
+    """
+    return clf.predict(x_test)
+
+
+def xgb_pipeline(x_train, y_train, x_test, y_test):
+    """
+    :param x_train: the x-values we want to train on (2D numpy array)
+    :param y_train: the y-values that correspond to x_train (1D numpy array)
+    :param x_test:  the x-values we want to test on (2D numpy array)
+    :param y_test:  the y-values that correspond to x_test (1D numpy array)
+    :return: the roc auc score
+    """
+    clf = xgb_training(x_train, y_train)
+    y_pred = xgb_classification(clf, x_test)
+    roc_results(y_pred, y_test, 'XGBoost')
+    return roc_auc_score(y_test, y_pred), results(y_pred, y_test)