The FairMLRegression class in the hana_ml.algorithms.pal.fair_ml module is a machine learning model that aims to mitigate unfairness in predictions due to potential biases in data features such as sex, race, and age, and it provides various parameters to customize the model's behavior.
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Here is a Python code template for the `FairMLRegression` class:

```python
from hana_ml.algorithms.pal.fair_ml import FairMLRegression

# Initialize the FairMLRegression model
fair_ml = FairMLRegression(
    fair_bound=0.5,
    fair_submodel='HGBT',
    fair_constraint='bounded_group_loss',
    fair_loss_func='mse',
    fair_loss_func_for_constraint='mse',
    fair_num_max_iter=50,
    fair_num_min_iter=5,
    fair_learning_rate=0.02,
    fair_norm_bound=100,
    fair_threshold=0.0,
    fair_exclude_sensitive_variable=True
)

# Fit the model
fair_ml.fit(
    data=df,
    key=None,
    features=None,
    label=None,
    fair_sensitive_variable='gender',
    categorical_variable=None,
    thread_ratio=None
)

# Predict using the model
res = fair_ml.predict(
    data=df_predict,
    key=None,
    features=None,
    thread_ratio=None,
    model=None
)
```

Please replace `df` and `df_predict` with your actual dataframes. Also, you may need to adjust the parameters according to your needs.