--- a
+++ b/binary_classification/random_forests.py
@@ -0,0 +1,38 @@
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+from util import roc_results, results
+
+
+def rf_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: sklearn random forest classifier object that can now be used for predictions
+    """
+    clf = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=4, max_features='log2',
+                                 min_samples_split=25, min_samples_leaf=7, bootstrap=True, random_state=0)
+    clf.fit(x_train, y_train)
+    return clf
+
+
+def rf_classification(clf, x_test):
+    """
+    :param clf: trained sklearn random forest 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 rf_pipeline(x_train, y_train, x_test, y_test):
+    """
+    :param x_train: the x-values we want to train on (2D numpy array)
+    :param x_test: the y-values that correspond to x_train (1D numpy array)
+    :param y_train: 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 = rf_training(x_train, y_train)
+    y_pred = rf_classification(clf, x_test)
+    roc_results(y_pred, y_test, 'Random Forest')
+    return roc_auc_score(y_test, y_pred), results(y_pred, y_test)