--- a
+++ b/rsf/rsf_baseline.py
@@ -0,0 +1,38 @@
+"""
+Baseline random forest script.
+Leon Zheng.
+
+ToDo: choose the right feature and fine tune.
+"""
+
+
+import preprocessing
+from sksurv.ensemble import RandomSurvivalForest
+from sklearn.model_selection import cross_validate
+
+"""
+Reading data
+"""
+
+def load_data(features=None):
+    X_df, y_df, _ = preprocessing.load_owkin_data()
+    if features != None:
+        X_df = X_df[features]
+    X = X_df.to_numpy()
+    y = preprocessing.y_dataframe_to_rsf_input(y_df)
+    return X_df, y_df, X, y
+
+X_df, y_df, X, y = load_data()
+feature_name = list(X_df.columns.values)
+
+"""
+Train model
+"""
+params = {'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 10}
+rsf = RandomSurvivalForest(n_estimators = params['n_estimators'],
+                           min_samples_split = params['min_samples_split'],
+                           min_samples_leaf = params['min_samples_leaf'],
+                           max_features="sqrt",
+                           n_jobs=-1
+                           )
+print(cross_validate(rsf, X, y, cv=5))