This is the standard Hoeffding-bound categorical feature proposed in the paper below: More...
Public Types | |
| typedef CategoricalSplitInfo | SplitInfo |
| The type of split information required by the HoeffdingCategoricalSplit. More... | |
Public Member Functions | |
| HoeffdingCategoricalSplit (const size_t numCategories=0, const size_t numClasses=0) | |
| Create the HoeffdingCategoricalSplit given a number of categories for this dimension and a number of classes. More... | |
| HoeffdingCategoricalSplit (const size_t numCategories, const size_t numClasses, const HoeffdingCategoricalSplit &other) | |
| Create the HoeffdingCategoricalSplit given a number of categories for this dimension and a number of classes and another HoeffdingCategoricalSplit to take parameters from. More... | |
| void | EvaluateFitnessFunction (double &bestFitness, double &secondBestFitness) const |
| Given the points seen so far, evaluate the fitness function, returning the gain for the best possible split and the second best possible split. More... | |
| size_t | MajorityClass () const |
| Get the majority class seen so far. More... | |
| double | MajorityProbability () const |
| Get the probability of the majority class given the points seen so far. More... | |
| size_t | NumChildren () const |
| Return the number of children, if the node were to split. More... | |
template < typename Archive > | |
| void | serialize (Archive &ar, const uint32_t) |
| Serialize the categorical split. More... | |
| void | Split (arma::Col< size_t > &childMajorities, SplitInfo &splitInfo) |
| Gather the information for a split: get the labels of the child majorities, and initialize the SplitInfo object. More... | |
template < typename eT > | |
| void | Train (eT value, const size_t label) |
| Train on the given value with the given label. More... | |
Detailed Description
template<typenameFitnessFunction>
class mlpack::tree::HoeffdingCategoricalSplit< FitnessFunction >
This is the standard Hoeffding-bound categorical feature proposed in the paper below:
This class will track the sufficient statistics of the training points it has seen. The HoeffdingSplit class (and other related classes) can use this class to track categorical features and split decision tree nodes.
- Template Parameters
-
FitnessFunction Fitness function to use for calculating gain.
Definition at line 44 of file hoeffding_categorical_split.hpp.
Member Typedef Documentation
◆ SplitInfo
| typedef CategoricalSplitInfo SplitInfo |
The type of split information required by the HoeffdingCategoricalSplit.
Definition at line 48 of file hoeffding_categorical_split.hpp.
Constructor & Destructor Documentation
◆ HoeffdingCategoricalSplit() [1/2]
| HoeffdingCategoricalSplit | ( | const size_t | numCategories = 0, |
| const size_t | numClasses = 0 |
||
| ) |
Create the HoeffdingCategoricalSplit given a number of categories for this dimension and a number of classes.
- Parameters
-
numCategories Number of categories in this dimension. numClasses Number of classes in this dimension.
◆ HoeffdingCategoricalSplit() [2/2]
| HoeffdingCategoricalSplit | ( | const size_t | numCategories, |
| const size_t | numClasses, | ||
| const HoeffdingCategoricalSplit< FitnessFunction > & | other | ||
| ) |
Create the HoeffdingCategoricalSplit given a number of categories for this dimension and a number of classes and another HoeffdingCategoricalSplit to take parameters from.
In this particular case, there are no parameters to take, but this constructor is required by the HoeffdingTree class.
Member Function Documentation
◆ EvaluateFitnessFunction()
| void EvaluateFitnessFunction | ( | double & | bestFitness, |
| double & | secondBestFitness | ||
| ) | const |
Given the points seen so far, evaluate the fitness function, returning the gain for the best possible split and the second best possible split.
In this splitting technique, we only split one possible way, so secondBestFitness will always be 0.
- Parameters
-
bestFitness The fitness function result for this split. secondBestFitness This is always set to 0 (this split only splits one way).
◆ MajorityClass()
| size_t MajorityClass | ( | ) | const |
Get the majority class seen so far.
Referenced by HoeffdingCategoricalSplit< FitnessFunction >::NumChildren().
◆ MajorityProbability()
| double MajorityProbability | ( | ) | const |
Get the probability of the majority class given the points seen so far.
Referenced by HoeffdingCategoricalSplit< FitnessFunction >::NumChildren().
◆ NumChildren()
|
inline |
Return the number of children, if the node were to split.
Definition at line 93 of file hoeffding_categorical_split.hpp.
References HoeffdingCategoricalSplit< FitnessFunction >::MajorityClass(), HoeffdingCategoricalSplit< FitnessFunction >::MajorityProbability(), and HoeffdingCategoricalSplit< FitnessFunction >::Split().
◆ serialize()
|
inline |
Serialize the categorical split.
Definition at line 111 of file hoeffding_categorical_split.hpp.
◆ Split()
| void Split | ( | arma::Col< size_t > & | childMajorities, |
| SplitInfo & | splitInfo | ||
| ) |
Gather the information for a split: get the labels of the child majorities, and initialize the SplitInfo object.
- Parameters
-
childMajorities Majorities of child nodes to be created. splitInfo Information for splitting.
Referenced by HoeffdingCategoricalSplit< FitnessFunction >::NumChildren().
◆ Train()
| void Train | ( | eT | value, |
| const size_t | label | ||
| ) |
Train on the given value with the given label.
- Parameters
-
value Value to train on. label Label to train on.
The documentation for this class was generated from the following file:
- /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/hoeffding_trees/hoeffding_categorical_split.hpp