24 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}))24.1 :xgboost/binary-hinge-loss
24.2 :xgboost/classification
24.3 :xgboost/count-poisson
24.4 :xgboost/gamma-regression
24.5 :xgboost/gpu-binary-logistic-classification
24.6 :xgboost/gpu-binary-logistic-raw-classification
24.7 :xgboost/gpu-linear-regression
24.8 :xgboost/gpu-logistic-regression
24.9 :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 |
24.10 :xgboost/logistic-binary-classification
24.11 :xgboost/logistic-binary-raw-classification
24.12 :xgboost/logistic-regression
24.13 :xgboost/multiclass-softmax
24.14 :xgboost/multiclass-softprob
24.15 :xgboost/rank-map
24.16 :xgboost/rank-ndcg
24.17 :xgboost/rank-pairwise
24.18 :xgboost/regression
24.19 :xgboost/squared-error-regression
24.20 :xgboost/survival-cox
24.21 :xgboost/tweedie-regression
source: notebooks/noj_book/xgboost.clj