feature_importance

main.py

@@ -145,7 +145,7 @@ class LightGBM(object):
             X_test[key] = pd.to_numeric(X_test[key], errors="coerce")
         bst = lgb.Booster(model_file=self.model)
         y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)
-        y_pred_binary = [0 if i <= 0.162294 else 1 for i in list(y_pred)]
+        y_pred_binary = [0 if i <= 0.164189 else 1 for i in list(y_pred)]
         # 转换为二进制输出
         score_list = []
         for index, item in enumerate(list(y_pred)):
@@ -181,6 +181,6 @@ class LightGBM(object):
 
 if __name__ == "__main__":
     L = LightGBM()
-    L.train_model()
+    # L.train_model()
     L.evaluate_model()
     L.feature_importance()