The RandomBinaryNumericSplit is a splitting function for decision trees that will split based on a randomly selected point between the minimum and maximum value of the numerical dimension. 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, const bool splitIfBetterGain=false) |
| Check if we can split a node. More... | |
| template<bool UseWeights, typename VecType , typename WeightVecType > | |
| static double | SplitIfBetter (const double bestGain, const VecType &data, const arma::rowvec &responses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, double &splitInfo, AuxiliarySplitInfo &aux, FitnessFunction &fitnessFunction, const bool splitIfBetterGain=false) |
| Check if we can split a node. More... | |
Detailed Description
template<typenameFitnessFunction>
class mlpack::tree::RandomBinaryNumericSplit< FitnessFunction >
The RandomBinaryNumericSplit is a splitting function for decision trees that will split based on a randomly selected point between the minimum and maximum value of the numerical dimension.
- Template Parameters
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FitnessFunction Fitness function to use to calculate gain.
Definition at line 28 of file random_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
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point Point to calculate direction of. splitInfo Auxiliary information for the split. * (aux) Auxiliary information for the split (Unused).
Referenced by RandomBinaryNumericSplit< FitnessFunction >::NumChildren().
◆ NumChildren()
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inlinestatic |
Returns 2, since the binary split always has two children.
- Parameters
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splitInfo Auxiliary information for the split. aux Auxiliary split information, which may be modified on a successful split.
Definition at line 136 of file random_binary_numeric_split.hpp.
References RandomBinaryNumericSplit< FitnessFunction >::CalculateDirection().
◆ SplitIfBetter() [1/2]
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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
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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. splitIfBetterGain When set to true, it will split only when gain is better than the current best gain. Otherwise, it always makes a split regardless of gain.
◆ SplitIfBetter() [2/2]
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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. splitIfBetterGain When set to true, it will split only when gain is better than the current best gain. Otherwise, it always makes a split regardless of gain.
The documentation for this class was generated from the following file:
- /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp