13 #ifndef MLPACK_METHODS_DET_DT_UTILS_HPP
14 #define MLPACK_METHODS_DET_DT_UTILS_HPP
34 const arma::mat& data,
35 const arma::Mat<size_t>& labels,
36 const size_t numClasses,
37 const std::string leafClassMembershipFile =
"");
48 const std::string viFile =
"");
64 const bool useVolumeReg =
false,
65 const size_t maxLeafSize = 10,
66 const size_t minLeafSize = 5,
67 const std::string unprunedTreeOutput =
"");
72 #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...
The core includes that mlpack expects; standard C++ includes and Armadillo.
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...