23 Xgboost model reference
As discussed in the Machine Learning chapter, this book contains reference chapters for machine learning models that can be registered in metamorph.ml.
This specific chapter focuses on the XGBoost algorithm provided by scicloj.ml.xgboost.
In the following we have a list of all model keys of Xgboost models including parameters. They can be used like this:
(comment
(ml/train df
{:model-type <model-key>
:param-1 0
:param-2 1}))23.1 :xgboost/binary-hinge-loss
23.2 :xgboost/classification
23.3 :xgboost/count-poisson
23.4 :xgboost/gamma-regression
23.5 :xgboost/gpu-binary-logistic-classification
23.6 :xgboost/gpu-binary-logistic-raw-classification
23.7 :xgboost/gpu-linear-regression
23.8 :xgboost/gpu-logistic-regression
23.9 :xgboost/linear-regression
| name | type | default | description |
|---|---|---|---|
| gamma | |||
| max-depth | |||
| min-child-weight | |||
| max-delta-step | |||
| subsample | |||
| sampling-method | |||
| colsample-bytree | |||
| colsample-bylevel | |||
| colsample-bynode | |||
| lambda | |||
| alpha | |||
| tree-method | |||
| sketch-eps | |||
| scale-pos-weight | |||
| updater | |||
| refresh-leaf | |||
| process-type | |||
| grow-policy | |||
| max-leaves | |||
| max-bin | |||
| predictor | |||
| num-parallel-tree | |||
| monotone-constraints | |||
| interaction-constraints |
23.10 :xgboost/logistic-binary-classification
23.11 :xgboost/logistic-binary-raw-classification
23.12 :xgboost/logistic-regression
23.13 :xgboost/multiclass-softmax
23.14 :xgboost/multiclass-softprob
23.15 :xgboost/rank-map
23.16 :xgboost/rank-ndcg
23.17 :xgboost/rank-pairwise
23.18 :xgboost/regression
23.19 :xgboost/squared-error-regression
23.20 :xgboost/survival-cox
23.21 :xgboost/tweedie-regression
source: notebooks/noj_book/xgboost.clj