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| author | Daniel Friesel <daniel.friesel@uos.de> | 2022-01-27 10:20:17 +0100 |
|---|---|---|
| committer | Daniel Friesel <daniel.friesel@uos.de> | 2022-01-27 10:20:17 +0100 |
| commit | 937bcec1ed1bd379c226aea5eb8ce5ec95264703 (patch) | |
| tree | d9da5b2102481e1916808031ce31d28fbe8980d5 /README.md | |
| parent | e149c6bc24935ff8383471759c8775d3174ec29d (diff) | |
add LMT support via https://github.com/cerlymarco/linear-tree
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 1 |
1 files changed, 1 insertions, 0 deletions
@@ -32,6 +32,7 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_DTREE_ENABLED` | 0, **1** | Use decision trees in get\_fitted | | `DFATOOL_DTREE_FUNCTION_LEAVES` | 0, **1** | Use functions (fitted via linear regression) in decision tree leaves when modeling numeric parameters with at least three distinct values. If 0, integer parameters are treated as enums instead. | | `DFATOOL_DTREE_SKLEARN_CART` | **0**, 1 | Use sklearn CART ("Decision Tree Regression") algorithm for decision tree generation. Uses binary nodes and supports splits on scalar variables. Overrides `FUNCTION_LEAVES` (=0) and `NONBINARY_NODES` (=0). | +| `DFATOOL_DTREE_LMT` | **0**, 1 | Use [Linear Model Tree](https://github.com/cerlymarco/linear-tree) algorithm for regression tree generation. Uses binary nodes and linear functions. Overrides `FUNCTION_LEAVES` (=0) and `NONBINARY_NODES` (=0). | | `DFATOOL_CART_MAX_DEPTH` | **0** .. *n* | maximum depth for sklearn CART. Default: unlimited. | | `DFATOOL_USE_XGBOOST` | **0**, 1 | Use Extreme Gradient Boosting algorithm for decision forest generation. | | `DFATOOL_XGB_N_ESTIMATORS` | 1 .. **100** .. *n* | Number of estimators (i.e., trees) for XGBoost. | |