score belong to different target classes. class 0 when init_score is given else 1

[i-plusplus]

Description

I am trying to train a LGBMClassfier with init_score for rare event prediction(CTR). Close to 99% of data is 0 and 1% of data is 1.
From the documentation it is not clear for which class init_score is expected.
If I give init_score while training the model, lightgbm assumes 0 to be positive event and generate raw_score for 0.
If I don't give init_score while training the model, lightgbm assumes 1 to be positive event and generate raw_score for 1.

Reproducible example

params['metric'] = 'binary_logloss'
clf4 = lgb.LGBMClassifier(n_jobs=-1)
clf4.fit(X=df[catf + numf], y = df.clicks, sample_weight=df.weight.values, categorical_feature=catf, init_score = init_score)

Environment info

LightGBM version or commit hash: Lightgbm version: '4.3.0'

Is there any way to force Lightgbm to generate the score for 1 instead of 0.

[jmoralez]

Hey @i-plusplus, thanks for using LightGBM. On binary classification the init score is expected to be given for the positive class.

If I give init_score while training the model, lightgbm assumes 0 to be positive event and generate raw_score for 0.

Can you provide a small, reproducible example of that? Your example is missing the data, parameters, weights, etc.

[i-plusplus]

Thanks for the response @jmoralez .

It was not the issue in lightgbm. It was a issue the way I was converting the values. Sorry about this.