<jupyter_start><jupyter_text>Expanded InductionLearn ensembles, expand those trees to MERCS as a whole.  Preliminaries<jupyter_code>import mercs
import numpy as np
from mercs.tests import load_iris, default_dataset
from mercs.core import Mercs<jupyter_output><empty_output><jupyter_text>Setup<jupyter_code>train, test = default_dataset(n_features=4)<jupyter_output><empty_output><jupyter_text>Sandbox<jupyter_code>clf = Mercs(
    max_depth=3,
    classifier_algorithm="XGB",
    regressor_algorithm="XGB",
    selection_algorithm="random",
    induction_algorithm="expand",
    fraction_missing=0.,
    nb_targets=1,
    nb_iterations=1,
    n_jobs=1,
    verbose=0,
    inference_algorithm="own",
    n_estimators=30,
    max_steps=8,
    prediction_algorithm="rw",
    random_state=800,
)
clf.fit(train)
clf.m_fimps
clf.m_list[0].model.get_booster().trees_to_dataframe()<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '565730', 'license': 'MIT', 'n_tokens_mistral': 353, 'n_tokens_neox': 335, 'n_words': 47, 'path': 'note/expand-induction/expand-induction.ipynb', 'repo_name': 'MattiasDC/mercs', 'size': '903'}