I am trying to integrate DAE with Xgboost, I have Successfully created both the DAE and XGB model, and I guess even integrated them, but I don’t know how should I take predictions:

```
scores = []
final_valid_predictions = {}
for train_idx, valid_idx in KFold().split(X, Y):
print("\nSOS\n")
model = XGBRegressor(
# random_state=fold,
tree_method='gpu_hist',
gpu_id=0,
predictor="gpu_predictor"
)
xtest = df_test.copy()
model.fit(X[train_idx], Y[train_idx])
preds_valid = model.predict(X[valid_idx])
# test_preds = model.predict(X_test)
print(X_test)
final_test_predictions.append(test_preds[gdcls])
final_valid_predictions.update(dict(zip(valid_ids, preds_valid)))
rmse = mean_squared_error(Y[valid_idx], preds_valid, squared=False)
print(fold, rmse)
scores.append(rmse)
print(np.mean(scores))
```

The model is broadly based on