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$begingroup$

People may or may not be able to help intuitively shed light on my problem. Maybe I haven't considered some aspects. I'm running a (kernel) regularized least squares estimation on some binary training data to then predict probabilistic values for the non-training data.

I have 9 variables in the original model, which I know should find the best predictions. I know this should be the right selection as I'm following a widely-cited scientific paper which justifies those 9 variables and no others.

However, when I add variables to the model, leave-one-out error, RMSE, MAE always all decrease. The pseudo R-squared always increases. This just shouldn't be happening, as far as I'm aware, as that implies that these models with more variables would explain the left-out data point data more accurately, and, therefore, the out-of-sample data too. Does anyone know any reason this might be happening?

prediction loss-function regularization error-handling

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asked 13 mins ago

user15881user15881

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user15881 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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0

$begingroup$

People may or may not be able to help intuitively shed light on my problem. Maybe I haven't considered some aspects. I'm running a (kernel) regularized least squares estimation on some binary training data to then predict probabilistic values for the non-training data.

I have 9 variables in the original model, which I know should find the best predictions. I know this should be the right selection as I'm following a widely-cited scientific paper which justifies those 9 variables and no others.

However, when I add variables to the model, leave-one-out error, RMSE, MAE always all decrease. The pseudo R-squared always increases. This just shouldn't be happening, as far as I'm aware, as that implies that these models with more variables would explain the left-out data point data more accurately, and, therefore, the out-of-sample data too. Does anyone know any reason this might be happening?

prediction loss-function regularization error-handling

share|improve this question

asked 13 mins ago

user15881user15881

1

New contributor

user15881 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 | 

$begingroup$

People may or may not be able to help intuitively shed light on my problem. Maybe I haven't considered some aspects. I'm running a (kernel) regularized least squares estimation on some binary training data to then predict probabilistic values for the non-training data.

I have 9 variables in the original model, which I know should find the best predictions. I know this should be the right selection as I'm following a widely-cited scientific paper which justifies those 9 variables and no others.

However, when I add variables to the model, leave-one-out error, RMSE, MAE always all decrease. The pseudo R-squared always increases. This just shouldn't be happening, as far as I'm aware, as that implies that these models with more variables would explain the left-out data point data more accurately, and, therefore, the out-of-sample data too. Does anyone know any reason this might be happening?

prediction loss-function regularization error-handling

share|improve this question

asked 13 mins ago

user15881user15881

1

New contributor

user15881 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

share|improve this question

asked 13 mins ago

user15881user15881

1

New contributor

user15881 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 13 mins ago

user15881user15881

1

New contributor

user15881 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 13 mins ago

user15881user15881

1

New contributor

user15881 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 13 mins ago

user15881user15881

1