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Ben Reiniger ♦
Ben Reiniger ♦
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I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]:
0:[f40<70.5] yes=1,no=2,missing=1
    1:leaf=0.00475667231
    2:leaf=0.154868156

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]: 0:[f40<70.5] yes=1,no=2,missing=1 1:leaf=0.00475667231 2:leaf=0.154868156 I have no idea how to interpret this in a way that makes sense with the prediction. What am I missing? Thanks!

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]:
0:[f40<70.5] yes=1,no=2,missing=1
    1:leaf=0.00475667231
    2:leaf=0.154868156

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]: 0:[f40<70.5] yes=1,no=2,missing=1 1:leaf=0.00475667231 2:leaf=0.154868156 I have no idea how to interpret this in a way that makes sense with the prediction. What am I missing? Thanks!

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]:
0:[f40<70.5] yes=1,no=2,missing=1
    1:leaf=0.00475667231
    2:leaf=0.154868156

I have no idea how to interpret this in a way that makes sense with the prediction. What am I missing? Thanks!

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L Xandor
L Xandor
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Understanding python XGBoost model dump output of a very simple tree

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]:
0:[f40<70.5] yes=1,no=2,missing=1
    1:leaf=0.00475667231
    2:leaf=0.154868156

I am trying to understand the model dump output from XGBoost. I would like to step through and see exactly how the model arrived at it's prediction. To simplify I trained a model with 1 tree and 1 max depth, and as expected all records get one of two predictions as it has a single split - the values are {0.5386398434638977, 0.5011891722679138}. However, when I look at the model dump I see the following

booster[0]: 0:[f40<70.5] yes=1,no=2,missing=1 1:leaf=0.00475667231 2:leaf=0.154868156 I have no idea how to interpret this in a way that makes sense with the prediction. What am I missing? Thanks!