what does regularization mean in xgboost (tree)
$begingroup$
In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.
Can someone explain how regularization works in xgbtree?
r regularization xgboost
edited 1 hour ago
kiamlaluno
1034
asked 5 hours ago
zeslazesla
1978
$endgroup$
add a comment |
$begingroup$
In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.
Can someone explain how regularization works in xgbtree?
r regularization xgboost
edited 1 hour ago
kiamlaluno
1034
asked 5 hours ago
zeslazesla
1978
$endgroup$
add a comment |
$begingroup$
In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.
Can someone explain how regularization works in xgbtree?
r regularization xgboost
edited 1 hour ago
kiamlaluno
1034
asked 5 hours ago
zeslazesla
1978
$endgroup$
In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.
Can someone explain how regularization works in xgbtree?
r regularization xgboost
r regularization xgboost
edited 1 hour ago
kiamlaluno
1034
asked 5 hours ago
zeslazesla
1978
edited 1 hour ago
kiamlaluno
1034
asked 5 hours ago
zeslazesla
1978
edited 1 hour ago
kiamlaluno
1034
edited 1 hour ago
kiamlaluno
1034
edited 1 hour ago
kiamlaluno
1034
1034
asked 5 hours ago
zeslazesla
1978
asked 5 hours ago
zeslazesla
1978
asked 5 hours ago
zeslazesla
1978
1978
add a comment |
add a comment |
1 Answer
1
active
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votes
$begingroup$
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:
Minimum loss reduction required to make a further partition on a leaf
node of the tree. The larger gamma is, the more conservative the
algorithm will be.
Source: https://xgboost.readthedocs.io/en/latest/parameter.html
This minimum gain can usually be set for anything between $(0,infty)$.
Here's a somewhat good article on how to tune regularization on XGBoost.
answered 4 hours ago
Lucas FariasLucas Farias
642421
$endgroup$
add a comment |
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1 Answer
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active
oldest
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1 Answer
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$begingroup$
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:
Minimum loss reduction required to make a further partition on a leaf
node of the tree. The larger gamma is, the more conservative the
algorithm will be.
Source: https://xgboost.readthedocs.io/en/latest/parameter.html
This minimum gain can usually be set for anything between $(0,infty)$.
Here's a somewhat good article on how to tune regularization on XGBoost.
answered 4 hours ago
Lucas FariasLucas Farias
642421
$endgroup$
add a comment |
$begingroup$
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:
Minimum loss reduction required to make a further partition on a leaf
node of the tree. The larger gamma is, the more conservative the
algorithm will be.
Source: https://xgboost.readthedocs.io/en/latest/parameter.html
This minimum gain can usually be set for anything between $(0,infty)$.
Here's a somewhat good article on how to tune regularization on XGBoost.
answered 4 hours ago
Lucas FariasLucas Farias
642421
$endgroup$
add a comment |
$begingroup$
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:
Minimum loss reduction required to make a further partition on a leaf
node of the tree. The larger gamma is, the more conservative the
algorithm will be.
Source: https://xgboost.readthedocs.io/en/latest/parameter.html
This minimum gain can usually be set for anything between $(0,infty)$.
Here's a somewhat good article on how to tune regularization on XGBoost.
answered 4 hours ago
Lucas FariasLucas Farias
642421
$endgroup$
In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:
Minimum loss reduction required to make a further partition on a leaf
node of the tree. The larger gamma is, the more conservative the
algorithm will be.
Source: https://xgboost.readthedocs.io/en/latest/parameter.html
This minimum gain can usually be set for anything between $(0,infty)$.
Here's a somewhat good article on how to tune regularization on XGBoost.
answered 4 hours ago
Lucas FariasLucas Farias
642421
edited 4 hours ago
answered 4 hours ago
Lucas FariasLucas Farias
642421
answered 4 hours ago
Lucas FariasLucas Farias
642421
answered 4 hours ago
Lucas FariasLucas Farias
642421
642421
add a comment |
add a comment |
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