XGBoost classifier XGBClassifier min_child_weight: what is it?
We have a simpliest train sample:
X y
0 "a"
1 "b"
And 2 simpliest classifiers with only one different last parameter min_child_weight:
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.25)
and
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.250001)
If we fit and then try to predict probabilities on the same sample (predict_proba), we will get:
array([[0.8807971 , 0.11920292],
[0.11920297, 0.880797 ]], dtype=float32)
and
array([[0.5, 0.5],
[0.5, 0.5]], dtype=float32
respectively. That means, that if min_child_weight>0.25, than we start facing underfitting.
Can anyone please tell, where does this 0.25 appears from? It is very simple example, so it seems that it can be easily deducted.
python xgboost xgbclassifier
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
add a comment |
We have a simpliest train sample:
X y
0 "a"
1 "b"
And 2 simpliest classifiers with only one different last parameter min_child_weight:
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.25)
and
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.250001)
If we fit and then try to predict probabilities on the same sample (predict_proba), we will get:
array([[0.8807971 , 0.11920292],
[0.11920297, 0.880797 ]], dtype=float32)
and
array([[0.5, 0.5],
[0.5, 0.5]], dtype=float32
respectively. That means, that if min_child_weight>0.25, than we start facing underfitting.
Can anyone please tell, where does this 0.25 appears from? It is very simple example, so it seems that it can be easily deducted.
python xgboost xgbclassifier
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22
add a comment |
We have a simpliest train sample:
X y
0 "a"
1 "b"
And 2 simpliest classifiers with only one different last parameter min_child_weight:
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.25)
and
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.250001)
If we fit and then try to predict probabilities on the same sample (predict_proba), we will get:
array([[0.8807971 , 0.11920292],
[0.11920297, 0.880797 ]], dtype=float32)
and
array([[0.5, 0.5],
[0.5, 0.5]], dtype=float32
respectively. That means, that if min_child_weight>0.25, than we start facing underfitting.
Can anyone please tell, where does this 0.25 appears from? It is very simple example, so it seems that it can be easily deducted.
python xgboost xgbclassifier
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
We have a simpliest train sample:
X y
0 "a"
1 "b"
And 2 simpliest classifiers with only one different last parameter min_child_weight:
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.25)
and
XGBClassifier(n_estimators=1, max_depth=1,
reg_lambda=0, learning_rate=1, min_child_weight=0.250001)
If we fit and then try to predict probabilities on the same sample (predict_proba), we will get:
array([[0.8807971 , 0.11920292],
[0.11920297, 0.880797 ]], dtype=float32)
and
array([[0.5, 0.5],
[0.5, 0.5]], dtype=float32
respectively. That means, that if min_child_weight>0.25, than we start facing underfitting.
Can anyone please tell, where does this 0.25 appears from? It is very simple example, so it seems that it can be easily deducted.
python xgboost xgbclassifier
python xgboost xgbclassifier
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
edited Dec 28 '18 at 13:06
Rohan Nadagouda
271213
271213
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
asked Nov 13 '18 at 17:40
Evgeniy1089Evgeniy1089
1
1
See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22
add a comment |
See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22
See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22
See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22
add a comment |
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See related: stats.stackexchange.com/questions/317073/…
– Bar
Nov 19 '18 at 14:22