Generalized Linear Models (iai_glm)

This model class supports a variety of regression models. Examples include linear regression, logistic regression, Poisson regression, gamma regression and inverse Gaussian regression models. We also support regularizing the model coefficients with the elastic net penalty.

Examples of use cases include [1]:

• Agriculture / weather modeling: number of rain events per year, amount of rainfall per event, total rainfall per year

• Risk modeling / insurance policy pricing: number of claim events / policyholder per year, cost per event, total cost per policyholder per year

• Predictive maintenance: number of production interruption events per year, duration of interruption, total interruption time per year

Example Model Configuration

model_config = {
    "experiment_name": "test_synthetic_tabular",
    "experiment_description": "test_synthetic_tabular",
    "strategy": {"name": "FedAvg", "params": {}},
    "model": {"params": { "input_size": 15,"output_activation": "sigmoid"}},
    "balance_train_datasets": False,
    "ml_task": {
        "type": "logistic",
        "params": {
            "loss_weights": None,
        },
    },
    "optimizer": {"name": "SGD", "params": {"learning_rate": 0.2, "momentum": 0.0}},
    "differential_privacy_params": {"epsilon": 4, "max_grad_norm": 7},
    "save_best_model": {
        "metric": "loss",  # to disable this and save model from the last round, set to None
        "mode": "min",
    },
    "seed": 23,  # for reproducibility
}

References [1]: https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression