Description

This is a feature wanted. Till latest version of sklearn, the MultiOutputRegressor.fit only support a optional sample_weight parameter. It would be nice if it support another optional fit_param parameter, which will enhance the estimator.fit. For example, we can use lightgbm or xgboost early stopping fitting way to overcome the over-fitting issue.

I know it is a little bit complicated to realize that. But I I hope you will consider that. Thanks!

Steps/Code to Reproduce

This is my expected usage example.

#!/usr/bin/env python3

import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import lightgbm as lgb

train_X = np.random.random((10, 10))
train_y = np.random.random((10, 4))
eval_X = np.random.random((10, 10))
train_y = np.random.random((10, 4))
single_model = lgb.GBMRegressor()
model = MultiOutputRegressor(single_model)
fit_param = {'verbose': False, 'early_stopping_rounds':10, 'eval_set':(eval_X, eval_y)}
reg.fit(train_X, train_y, fit_param=fit_param)

Expected Results

Unsupported yet.

Actual Results

Unsupported yet.

Versions

Scikit-Learn: 0.22
pltaform: Windows-10-10.0.14393-SP0
python: 3.6.9