// desc = this can reduce the effect of noises in categorical features, especially for categories with few data
// desc = this can reduce the effect of noises in categorical features, especially for categories with few data
doublecat_smooth=10.0;
doublecat_smooth=10.0;
// check = >0
// check = >0
// desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used
// desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used
intmax_cat_to_onehot=4;
intmax_cat_to_onehot=4;
...
@@ -362,7 +362,7 @@ public:
...
@@ -362,7 +362,7 @@ public:
// desc = used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature
// desc = used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature
// desc = you need to specify all features in order
// desc = you need to specify all features in order
std::vector<double>feature_contri;
std::vector<double>feature_contri;
// alias = fs, forced_splits_filename, forced_splits_file, forced_splits
// alias = fs, forced_splits_filename, forced_splits_file, forced_splits
// desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences
// desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences
// desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits
// desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits