80 int cfg_max_depth = -1,
81 int cfg_max_leaves = -1,
82 float cfg_max_features = 1.0f,
83 int cfg_max_n_bins = 128,
84 int cfg_min_samples_leaf = 1,
85 int cfg_min_samples_split = 2,
86 float cfg_min_impurity_decrease = 0.0f,
88 int cfg_max_batch_size = 4096);
90 template <
class T,
class L>
108 template <
class T,
class L>
118 template <
class T,
class L>
128 template <
class T,
class L>
Definition: params.hpp:17
TreeMetaDataNode< double, double > TreeRegressorD
Definition: decisiontree.hpp:134
void set_tree_params(DecisionTreeParams ¶ms, int cfg_max_depth=-1, int cfg_max_leaves=-1, float cfg_max_features=1.0f, int cfg_max_n_bins=128, int cfg_min_samples_leaf=1, int cfg_min_samples_split=2, float cfg_min_impurity_decrease=0.0f, CRITERION cfg_split_criterion=CRITERION_END, int cfg_max_batch_size=4096)
Set all DecisionTreeParams members.
std::string get_tree_text(const TreeMetaDataNode< T, L > *tree)
Obtain detailed tree information.
std::string get_tree_json(const TreeMetaDataNode< T, L > *tree)
Export tree as a JSON string.
std::string get_tree_summary_text(const TreeMetaDataNode< T, L > *tree)
Obtain high-level tree information.
TreeMetaDataNode< double, int > TreeClassifierD
Definition: decisiontree.hpp:132
TreeMetaDataNode< float, float > TreeRegressorF
Definition: decisiontree.hpp:133
TreeMetaDataNode< float, int > TreeClassifierF
Definition: decisiontree.hpp:131
Definition: dbscan.hpp:18
CRITERION
Definition: algo_helper.h:9
@ CRITERION_END
Definition: algo_helper.h:17
Definition: decisiontree.hpp:18
int max_batch_size
Definition: decisiontree.hpp:58
int max_n_bins
Definition: decisiontree.hpp:35
CRITERION split_criterion
Definition: decisiontree.hpp:47
float max_features
Definition: decisiontree.hpp:31
int min_samples_leaf
Definition: decisiontree.hpp:39
int max_depth
Definition: decisiontree.hpp:23
float min_impurity_decrease
Definition: decisiontree.hpp:52
int min_samples_split
Definition: decisiontree.hpp:43
int max_leaves
Definition: decisiontree.hpp:27