The GradientBoostingRegressor class in the hana_ml.algorithms.pal.trees module is a Gradient Boosting Tree model for regression, with various parameters for customization such as the number of trees, loss function type, maximum depth of a tree, learning rate, and more.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.trees import GradientBoostingRegressor

# Create GradientBoostingRegressor instance
gbr = GradientBoostingRegressor(
    n_estimators=20, 
    split_threshold=0.75,
    learning_rate=0.75, 
    fold_num=5, 
    max_depth=6,
    cv_metric='rmse', 
    ref_metric=['mae'],
    cross_validation_range=[
        ('learning_rate',[0.0,5,1.0]), 
        ('n_estimators', [10, 11, 20]), 
        ('split_threshold', [0.0, 5, 1.0])
    ]
)

# Assume df is your training data DataFrame
# Perform fit() on given dataframe
gbr.fit(data=df, features=['ATT1', 'ATT2', 'ATT3', 'ATT4'], label='TARGET')

# Print model statistics
print(gbr.stats_.collect())

# Assume df1 is your prediction data DataFrame
# Perform predict() on given dataframe
result = gbr.predict(data=df1)

# Print prediction results
print(result.head(4).collect())
```

Please replace `df` and `df1` with your actual DataFrame objects. The `features` parameter in the `fit` method should be a list of your feature column names, and the `label` parameter should be the name of your target column. Similarly, in the `predict` method, replace `data=df1` with your actual test DataFrame.