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erdogant / hgboost

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.
http://erdogant.github.io/hgboost
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issue with shuffling results and loading model #8

Closed chennavc closed 2 years ago

chennavc commented 2 years ago
  1. R2, RMSE,MAE, MAPE results were changing. so it is difficult to compare different experiments.
  2. I am unable to load the saved model.
erdogant commented 2 years ago

Can you show both issues with an example?

erdogant commented 2 years ago

During initialization, you can set the random_state. Have you tried that already?

from hgboost import hgboost
hg = hgboost(random_state=0)
results = hg.xgboost(X, y)
erdogant commented 2 years ago

Reopen this issue and let me know if you had something else in mind.