def exrf(train_sample, validation_sample, features, seed):
log_base = np.e
exrf_est = ExtraTreesRegressor(n_estimators=1000,
criterion='mse',
max_features='auto',
max_depth=None,
bootstrap=True,
min_samples_split=4,
min_samples_leaf=1,
min_weight_fraction_leaf=0,
max_leaf_nodes=None,
random_state=seed
).fit(
train_sample[features], np.log1p(train_sample['volume']) / np.log(log_base))
exrf_prob = np.power(log_base, exrf_est.predict(validation_sample[features])) - 1
print_mape(validation_sample['volume'], exrf_prob, 'EXTRA-RF')
return exrf_prob
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