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I'm using LGMRanker from LightGBM but not sure what evaluation metric I should be using. Here is my code:

import lightgbm as lgb


gbm = lgb.LGBMRanker
gridParams = {
'learning_rate': [0.005,0.01,0.02],
'max_depth': [5,6,7],
'n_estimators': [100,200],
'num_leaves': [20,30,50]
}
lgb_grid = GridSearchCV(estimator = gbm, param_grid = gridParams, scoring = '??', cv = 3, verbose=2, n_jobs = -1)

What' appropriate here? I don't have any group, should I specify something?

HHH
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1 Answers1

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DCG and NDCG are good evaluation methods for ranking algorithms. They can help you measure the quality of your results. You can read about them here. https://machinelearningmedium.com/2017/07/24/discounted-cumulative-gain/

P.Gupta
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