diff options
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/analysis-nfp.md | 2 | ||||
| -rw-r--r-- | doc/model-visual.md | 2 | ||||
| -rw-r--r-- | doc/modeling-method.md | 8 |
3 files changed, 6 insertions, 6 deletions
diff --git a/doc/analysis-nfp.md b/doc/analysis-nfp.md index cec5ad0..877ac2a 100644 --- a/doc/analysis-nfp.md +++ b/doc/analysis-nfp.md @@ -8,7 +8,7 @@ Classification and Regression Trees (CART) are capable of generating accurate mo Hence, after loading a CART model into kconfig-webconf, only a small subset of busybox features will be annotated with NFP deltas. ``` -DFATOOL_DTREE_SKLEARN_CART=1 DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 .../dfatool/bin/analyze-kconfig.py --export-webconf busybox.json --force-tree ../busybox-1.35.0/Kconfig . +DFATOOL_DTREE_SKLEARN_CART=1 DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 .../dfatool/bin/analyze-kconfig.py --export-webconf busybox.json --force-tree ../busybox-1.35.0/Kconfig . ``` Refer to the [kconfig-webconf README](https://ess.cs.uos.de/git/software/kconfig-webconf/-/blob/master/README.md#user-content-performance-aware-configuration) for details on using the generated model. diff --git a/doc/model-visual.md b/doc/model-visual.md index 2fdbd29..2bc38b4 100644 --- a/doc/model-visual.md +++ b/doc/model-visual.md @@ -48,7 +48,7 @@ The model for `param[paramIndex] <= threshold` is located in `left`, the model for `param[paramIndex] > threshold` is located in `right`. `value` is a static model that serves as fall-back if `param[paramIndex]` is undefined. -### RMT categorial split node +### RMT categorical split node ``` { diff --git a/doc/modeling-method.md b/doc/modeling-method.md index 27cb334..e4865d9 100644 --- a/doc/modeling-method.md +++ b/doc/modeling-method.md @@ -6,7 +6,7 @@ Enable these with `DFATOOL_DTREE_SKLEARN_CART=1` and `--force-tree`. ### Related Options -* `DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1` converts categorial parameters (which are not supported by CART) to numeric ones. +* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by CART) to numeric ones. ## XGB (Gradient-Boosted Forests / eXtreme Gradient boosting) @@ -15,7 +15,7 @@ You should also specify `DFATOOL_XGB_N_ESTIMATORS`, `DFATOOL_XGB_MAX_DEPTH`, and ### Related Options -* `DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1` converts categorial parameters (which are not supported by XGB) to numeric ones. +* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by XGB) to numeric ones. * Anything prefixed with `DFATOOL_XGB_`. ## LMT (Linear Model Trees) @@ -27,7 +27,7 @@ They always use a maximum depth of 20. See the [LinearTreeRegressor documentation](lib/lineartree/lineartree.py) for details on training hyper-parameters. -* `DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1` converts categorial parameters (which are not supported by LMT) to numeric ones. +* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` converts categorical parameters (which are not supported by LMT) to numeric ones. * `DFATOOL_LMT_MAX_DEPTH` * `DFATOOL_LMT_MIN_SAMPLES_SPLIT` * `DFATOOL_LMT_MIN_SAMPLES_LEAF` @@ -47,7 +47,7 @@ All of these are valid regression model trees. * `DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS=0` disables the relevant parameter detection heuristic when building the tree structure. By default, irrelevant parameters cannot end up as decision nodes. * `DFATOOL_FIT_LINEAR_ONLY=1` makes RMT behave more like LMT by only considering linear functions in leaf nodes. * `DFATOOL_FIT_FOL=1` -* `DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1` +* `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR=1` * `DFATOOL_ULS_SKIP_CODEPENDENT_CHECK=1` * `DFATOOL_REGRESSION_SAFE_FUNCTIONS=1` |