The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split. More...
Classes | |
| class | AuxiliarySplitInfo |
Static Public Member Functions | |
template < typename ElemType > | |
| static size_t | CalculateDirection (const ElemType &point, const double &splitInfo, const AuxiliarySplitInfo &) |
| Given a point, calculate which child it should go to (left or right). More... | |
| static size_t | NumChildren (const double &, const AuxiliarySplitInfo &) |
| Returns 2, since the binary split always has two children. More... | |
| template<bool UseWeights, typename VecType , typename WeightVecType > | |
| static double | SplitIfBetter (const double bestGain, const VecType &data, const arma::Row< size_t > &labels, const size_t numClasses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, arma::vec &splitInfo, AuxiliarySplitInfo &aux) |
| Check if we can split a node. More... | |
| template<bool UseWeights, typename VecType , typename ResponsesType , typename WeightVecType > | |
| static std::enable_if< !HasOptimizedBinarySplitForms< FitnessFunction, UseWeights >::value, double >::type | SplitIfBetter (const double bestGain, const VecType &data, const ResponsesType &responses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, double &splitInfo, AuxiliarySplitInfo &aux, FitnessFunction &fitnessFunction) |
| Check if we can split a node. More... | |
| template<bool UseWeights, typename VecType , typename ResponsesType , typename WeightVecType > | |
| static std::enable_if< HasOptimizedBinarySplitForms< FitnessFunction, UseWeights >::value, double >::type | SplitIfBetter (const double bestGain, const VecType &data, const ResponsesType &responses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, double &splitInfo, AuxiliarySplitInfo &, FitnessFunction &fitnessFunction) |
| Check if we can split a node. More... | |
Detailed Description
template<typenameFitnessFunction>
class mlpack::tree::BestBinaryNumericSplit< FitnessFunction >
The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split.
- Template Parameters
-
FitnessFunction Fitness function to use to calculate gain.
Definition at line 49 of file best_binary_numeric_split.hpp.
Member Function Documentation
◆ CalculateDirection()
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static |
Given a point, calculate which child it should go to (left or right).
- Parameters
-
point Point to calculate direction of. splitInfo Auxiliary information for the split. * (aux) Auxiliary information for the split (Unused).
◆ NumChildren()
|
inlinestatic |
Returns 2, since the binary split always has two children.
Definition at line 166 of file best_binary_numeric_split.hpp.
◆ SplitIfBetter() [1/3]
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static |
Check if we can split a node.
If we can split a node in a way that improves on 'bestGain', then we return the improved gain. Otherwise we return the value 'bestGain'. If a split is made, then splitInfo and aux may be modified.
This overload is used only for classification tasks.
- Parameters
-
bestGain Best gain seen so far (we'll only split if we find gain better than this). data The dimension of data points to check for a split in. labels Labels for each point. numClasses Number of classes in the dataset. weights Weights associated with labels. minimumLeafSize Minimum number of points in a leaf node for splitting. minimumGainSplit Minimum gain split. splitInfo Stores split information on a successful split. aux Auxiliary split information, which may be modified on a successful split.
◆ SplitIfBetter() [2/3]
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static |
Check if we can split a node.
If we can split a node in a way that improves on 'bestGain', then we return the improved gain. Otherwise we return the value 'bestGain'. If a split is made, then splitInfo and aux may be modified.
This overload is used only for regression tasks.
- Parameters
-
bestGain Best gain seen so far (we'll only split if we find gain better than this). data The dimension of data points to check for a split in. responses Responses for each point. weights Weights associated with responses. minimumLeafSize Minimum number of points in a leaf node for splitting. minimumGainSplit Minimum gain split. splitInfo Stores split information on a successful split. aux Auxiliary split information, which may be modified on a successful split. fitnessFunction The FitnessFunction object instance. It it used to evaluate the gain for the split.
◆ SplitIfBetter() [3/3]
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static |
Check if we can split a node.
If we can split a node in a way that improves on 'bestGain', then we return the improved gain. Otherwise we return the value 'bestGain'. If a split is made, then splitInfo and aux may be modified.
This overload is specialized for any fitness function that implements BinaryScanInitialize(), BinaryStep() and BinaryGains() functions.
- Parameters
-
bestGain Best gain seen so far (we'll only split if we find gain better than this). data The dimension of data points to check for a split in. responses Responses for each point. weights Weights associated with responses. minimumLeafSize Minimum number of points in a leaf node for splitting. minimumGainSplit Minimum gain split. splitInfo Stores split information on a successful split. aux Auxiliary split information, which may be modified on a successful split.
The documentation for this class was generated from the following file:
- /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp