Which model to chose based on learning curveEstimating expected revenue generationWill cross validation performance be an accurate indication for predicting the true performance on an independent data set?Model selection and assessment using leave-one-out cross validationOverfitting XGBoostValidation curve unlike SKLearn sampleValidation of a model generated by automated deep learning systemvery large difference between cross_val and (multiple) r2 model evaluationOptimizing decision threshold on model with oversampled/imbalanced dataBias-variance tradeoff in practice (CNN)How to recognise when to stop training based on Overfitting/Underfitting?
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Which model to chose based on learning curve
Estimating expected revenue generationWill cross validation performance be an accurate indication for predicting the true performance on an independent data set?Model selection and assessment using leave-one-out cross validationOverfitting XGBoostValidation curve unlike SKLearn sampleValidation of a model generated by automated deep learning systemvery large difference between cross_val and (multiple) r2 model evaluationOptimizing decision threshold on model with oversampled/imbalanced dataBias-variance tradeoff in practice (CNN)How to recognise when to stop training based on Overfitting/Underfitting?
$begingroup$
I trained my model using different regression techniques, and I'm not sure which model to choose based on the learning curve.
1) Should I choose Lasso, since train and CV converge at the end
2) Should I choose Gradient Boosting since train and CV converge in the middle, and achieves a lower RMSE score than Lasso
cross-validation overfitting
asked Apr 26 at 8:46
user13432user13432
132
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user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I trained my model using different regression techniques, and I'm not sure which model to choose based on the learning curve.
1) Should I choose Lasso, since train and CV converge at the end
2) Should I choose Gradient Boosting since train and CV converge in the middle, and achieves a lower RMSE score than Lasso
cross-validation overfitting
asked Apr 26 at 8:46
user13432user13432
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I trained my model using different regression techniques, and I'm not sure which model to choose based on the learning curve.
1) Should I choose Lasso, since train and CV converge at the end
2) Should I choose Gradient Boosting since train and CV converge in the middle, and achieves a lower RMSE score than Lasso
cross-validation overfitting
asked Apr 26 at 8:46
user13432user13432
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I trained my model using different regression techniques, and I'm not sure which model to choose based on the learning curve.
1) Should I choose Lasso, since train and CV converge at the end
2) Should I choose Gradient Boosting since train and CV converge in the middle, and achieves a lower RMSE score than Lasso
cross-validation overfitting
cross-validation overfitting
asked Apr 26 at 8:46
user13432user13432
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 26 at 8:46
user13432user13432
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 26 at 8:46
user13432user13432
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 26 at 8:46
user13432user13432
132
asked Apr 26 at 8:46
user13432user13432
132
132
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user13432 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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You should go for LightGBM which has the lowest training and cross validation RMSE. By the way, score is a bad name for RMSE because you would expect a higher score to mean that your model is better but here the opposite is true.
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
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1 Answer
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1 Answer
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$begingroup$
You should go for LightGBM which has the lowest training and cross validation RMSE. By the way, score is a bad name for RMSE because you would expect a higher score to mean that your model is better but here the opposite is true.
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
$endgroup$
add a comment |
$begingroup$
You should go for LightGBM which has the lowest training and cross validation RMSE. By the way, score is a bad name for RMSE because you would expect a higher score to mean that your model is better but here the opposite is true.
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
$endgroup$
add a comment |
$begingroup$
You should go for LightGBM which has the lowest training and cross validation RMSE. By the way, score is a bad name for RMSE because you would expect a higher score to mean that your model is better but here the opposite is true.
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
$endgroup$
You should go for LightGBM which has the lowest training and cross validation RMSE. By the way, score is a bad name for RMSE because you would expect a higher score to mean that your model is better but here the opposite is true.
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
answered Apr 26 at 10:27
Robin NicoleRobin Nicole
33611
33611
add a comment |
add a comment |
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