Description
Hi, as said in the title, I'm trying to update the lightgbm version to the latest, 4.6.0, as requested by the security department of my company.
At the step that is taking much longer, I'm using default parameters, so I didn't change the code at all:
estimator = lgb.LGBMRegressor(n_jobs=-1, verbosity=2)
Then, to ensure the parameters were the same as the default parameters of the 3.3.5 version, I forced them:
params = {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbosity': 2}
estimator = lgb.LGBMRegressor(**params)
The model in the 4.6.0 version is slowly iterating and using more features, but as I said, the time taken is absurd. I guess the difference lays in one of the default parameters the 4.6.0 has that I haven't changed with the parameters I passed, but I'm not sure.
I installed lightgbm through pip.
Does anyone have an idea what it might be? Thanks