Update app.py

app.py

@@ -115,7 +115,7 @@ def train_model(X,labels):
         return
     train_data = lgb.Dataset(X, label=labels)
     num_round = 10
-    param = {'num_leaves':50, 'objective': 'binary', 'metric' : 'auc', 'lambda_l2': 0.1, 'max_depth':5}
+    param = {'num_leaves':30, 'objective': 'binary', 'metric' : 'auc', 'lambda_l2': 0.1, 'max_depth':8}
     bst = lgb.train(param, train_data, num_round)
     in_sample_preds = bst.predict(X)
     in_sample_score = np.corrcoef([in_sample_preds,np.array(labels)])[0][1]