To compare the predicted values and real values of an XGBoost regressor model in Python, you can follow these steps:
main.py70 chars3 lines
main.py55 chars3 lines
Replace X_train
with your training feature dataset and y_train
with your training target variable.
main.py31 chars2 lines
Replace X_test
with your testing feature dataset.
main.py77 chars3 lines
Replace y_test
with your testing target variable.
By comparing the predicted and real values using evaluation metrics like MAE, you can assess the performance of your XGBoost regressor model.
Remember to replace the dataset variables (X_train
, y_train
, X_test
, y_test
) with your own dataset variables.
Note: The code above assumes that you have already preprocessed your data and split it into training and testing datasets.
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