Train the GBT
=============
For the gradient boosted trees model (GBT), the formatting of the training and evaluating dataset change a little, instead of provide numpy arrays, you should pass dictionnaries::

    def run_gbt():
        (train_inputs, train_targets, train_timestamps), (test_inputs, test_targets, test_timestamps) = split_data()
       
        # creating datasets for training and testing
        train_dataset = tf.data.Dataset.from_tensor_slices(
            (
                {name: train_inputs[:, i] for i, name in enumerate(INPUTS_VAR)},
                train_targets[:, 0],
            ),
        ).batch(250)

        test_dataset = tf.data.Dataset.from_tensor_slices(
            (
                {name: test_inputs[:, i] for i, name in enumerate(INPUTS_VAR)},
                test_targets[:, 0],
            ),
        ).batch(250)

Moreover, you can automatically adjust the hyperparameters by passing a ``tuner`` to the model generator::

    # creating a gradient boosted model
    tuner = tfdf.tuner.RandomSearch(num_trials=250, use_predefined_hps=True)
    model_set = nn.ModelSet(
        nb_models=1,
        model_generator=nn.gbt_model,
        tuner=tuner,
    )

The following steps are identical to these with an ANN::

    model_set.fit(train_dataset)
    model_set.evaluate(test_dataset)
    prediction = np.median(model_set(test_dataset)[:, 0], axis=0)

    utils.write_output(prediction, column=TARGETS_VAR)