XGBoost hyperparameters depend on number of samples, how can I avoid constantly retraining as I collect more...
$begingroup$
I'm currently using this:
XGBRegressor(
learning_rate=0.2, n_estimators=100, max_depth=6, min_split_loss=0.1, min_child_weight=1,
reg_alpha=2, reg_lambda=2, scale_pos_weight=1, nthread=3, subsample=0.5, colsample_bytree=0.5
)
With n samples, this works well. With 2n samples, it overfits. With n/2 samples, it underfits. I have to change the learning_rate and reg_alpha whenever I change the number of samples. Is there a systematic way to make the hyperparameters independent of the number of input samples?
I could just make the hyperparameters variables dependent on the number of samples, but I'm wondering if there's a better way to do so.
machine-learning xgboost
asked yesterday
Leo JiangLeo Jiang
1063
$endgroup$
add a comment |
$begingroup$
I'm currently using this:
XGBRegressor(
learning_rate=0.2, n_estimators=100, max_depth=6, min_split_loss=0.1, min_child_weight=1,
reg_alpha=2, reg_lambda=2, scale_pos_weight=1, nthread=3, subsample=0.5, colsample_bytree=0.5
)
With n samples, this works well. With 2n samples, it overfits. With n/2 samples, it underfits. I have to change the learning_rate and reg_alpha whenever I change the number of samples. Is there a systematic way to make the hyperparameters independent of the number of input samples?
I could just make the hyperparameters variables dependent on the number of samples, but I'm wondering if there's a better way to do so.
machine-learning xgboost
asked yesterday
Leo JiangLeo Jiang
1063
$endgroup$
add a comment |
$begingroup$
I'm currently using this:
XGBRegressor(
learning_rate=0.2, n_estimators=100, max_depth=6, min_split_loss=0.1, min_child_weight=1,
reg_alpha=2, reg_lambda=2, scale_pos_weight=1, nthread=3, subsample=0.5, colsample_bytree=0.5
)
With n samples, this works well. With 2n samples, it overfits. With n/2 samples, it underfits. I have to change the learning_rate and reg_alpha whenever I change the number of samples. Is there a systematic way to make the hyperparameters independent of the number of input samples?
I could just make the hyperparameters variables dependent on the number of samples, but I'm wondering if there's a better way to do so.
machine-learning xgboost
asked yesterday
Leo JiangLeo Jiang
1063
$endgroup$
I'm currently using this:
XGBRegressor(
learning_rate=0.2, n_estimators=100, max_depth=6, min_split_loss=0.1, min_child_weight=1,
reg_alpha=2, reg_lambda=2, scale_pos_weight=1, nthread=3, subsample=0.5, colsample_bytree=0.5
)
With n samples, this works well. With 2n samples, it overfits. With n/2 samples, it underfits. I have to change the learning_rate and reg_alpha whenever I change the number of samples. Is there a systematic way to make the hyperparameters independent of the number of input samples?
I could just make the hyperparameters variables dependent on the number of samples, but I'm wondering if there's a better way to do so.
machine-learning xgboost
machine-learning xgboost
asked yesterday
Leo JiangLeo Jiang
1063
asked yesterday
Leo JiangLeo Jiang
1063
asked yesterday
Leo JiangLeo Jiang
1063
asked yesterday
Leo JiangLeo Jiang
1063
asked yesterday
Leo JiangLeo Jiang
1063
1063
add a comment |
add a comment |
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