diff options
| -rw-r--r-- | README.md | 2 | ||||
| -rw-r--r-- | lib/parameters.py | 4 |
2 files changed, 3 insertions, 3 deletions
@@ -134,7 +134,7 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_FIT_LINEAR_ONLY` | **0**, 1 | Only consider linear functions (a + bx) in regression analysis. Useful for comparison with Linear Model Trees / M5. | | `DFATOOL_REGRESSION_SAFE_FUNCTIONS` | **0**, 1 | Use safe functions only (e.g. 1/x returnning 1 for x==0) | | `DFATOOL_DTREE_NONBINARY_NODES` | 0, **1** | Enable non-binary nodes (i.e., nodes with more than two children corresponding to enum variables) in decision trees | -| `DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS` | 0, **1** | Ignore parameters deemed irrelevant by stddev heuristic during regression tree generation | +| `DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS` | **0**, 1 | Ignore parameters deemed irrelevant by stddev heuristic during regression tree generation. Use with caution. | | `DFATOOL_PARAM_RELEVANCE_THRESHOLD` | 0 .. **0.5** .. 1 | Threshold for relevant parameter detection: parameter *i* is relevant if mean standard deviation (data partitioned by all parameters) / mean standard deviation (data partition by all parameters but *i*) is less than threshold | | `DFATOOL_DTREE_LOSS_IGNORE_SCALAR` | **0**, 1 | Ignore scalar parameters when computing the loss for split node candidates. Instead of computing the loss of a single partition for each `x_i == j`, compute the loss of partitions for `x_i == j` in which non-scalar parameters vary and scalar parameters are constant. This way, scalar parameters do not affect the decision about which non-scalar parameter to use for splitting. | | `DFATOOL_PARAM_CATEGORIAL_TO_SCALAR` | **0**, 1 | Some models (e.g. FOL, sklearn CART, XGBoost) do not support categorial parameters. Ignore them (0) or convert them to scalar indexes (1). | diff --git a/lib/parameters.py b/lib/parameters.py index 2095031..603e56d 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -923,7 +923,7 @@ class ModelAttribute: def build_fol_model(self): ignore_irrelevant = bool( - int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "1")) + int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "0")) ) ignore_param_indexes = list() if ignore_irrelevant: @@ -1048,7 +1048,7 @@ class ModelAttribute: with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0"))) if ignore_irrelevant_parameters is None: ignore_irrelevant_parameters = bool( - int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "1")) + int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "0")) ) if loss_ignore_scalar is None: loss_ignore_scalar = bool( |