Some utility functions to make ML with Python / Pandas / sklearn even easier
from mlgear.cv import run_cv_model
from mlgear.models import runLGB
from mlgear.metrics import rmse
lgb_params = {'application': 'regression',
'boosting': 'gbdt',
'metric': 'rmse',
'num_leaves': 15,
'learning_rate': 0.01,
'bagging_fraction': 0.9,
'feature_fraction': 0.9,
'verbosity': -1,
'seed': 1,
'lambda_l1': 1,
'lambda_l2': 1,
'early_stop': 20,
'verbose_eval': 10,
'num_rounds': 500,
'num_threads': 3}
results = run_cv_model(train, test, target, runLGB, lgb_params, rmse)
pip3 install mlgear