compare predicted values and real values of a xgboost regressor model in python

To compare the predicted values and real values of an XGBoost regressor model in Python, you can follow these steps:

  1. Import the necessary libraries:
main.py
import xgboost as xgb
from sklearn.metrics import mean_absolute_error
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  1. Fit the XGBoost regressor model on your training data:
main.py
model = xgb.XGBRegressor()
model.fit(X_train, y_train)
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Replace X_train with your training feature dataset and y_train with your training target variable.

  1. Make predictions on your testing data:
main.py
y_pred = model.predict(X_test)
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Replace X_test with your testing feature dataset.

  1. Compare the predicted values and real values using appropriate evaluation metrics. In this example, we will use the mean absolute error (MAE):
main.py
mae = mean_absolute_error(y_test, y_pred)
print('Mean Absolute Error:', mae)
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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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