23 #ifndef __MLPACK_METHODS_DET_DT_UTILS_HPP
24 #define __MLPACK_METHODS_DET_DT_UTILS_HPP
44 const arma::mat& data,
45 const arma::Mat<size_t>& labels,
46 const size_t numClasses,
47 const std::string leafClassMembershipFile =
"");
58 const std::string viFile =
"");
74 const bool useVolumeReg =
false,
75 const size_t maxLeafSize = 10,
76 const size_t minLeafSize = 5,
77 const std::string unprunedTreeOutput =
"");
82 #endif // __MLPACK_METHODS_DET_DT_UTILS_HPP
void PrintLeafMembership(DTree *dtree, const arma::mat &data, const arma::Mat< size_t > &labels, const size_t numClasses, const std::string leafClassMembershipFile="")
Print the membership of leaves of a density estimation tree given the labels and number of classes...
void PrintVariableImportance(const DTree *dtree, const std::string viFile="")
Print the variable importance of each dimension of a density estimation tree.
DTree * Trainer(arma::mat &dataset, const size_t folds, const bool useVolumeReg=false, const size_t maxLeafSize=10, const size_t minLeafSize=5, const std::string unprunedTreeOutput="")
Train the optimal decision tree using cross-validation with the given number of folds.
A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd...