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RandomForestOptions Class Reference | ![]() |
Options class for vigra::rf3::RandomForest version 3. More...
#include <vigra/random_forest_3/random_forest_common.hxx>
Public Member Functions | |
RandomForestOptions & | bootstrap_sampling (bool b) |
Use bootstrap sampling. More... | |
RandomForestOptions & | class_weights (std::vector< double > const &v) |
Each datapoint is weighted by its class weight. By default, each class has weight 1. More... | |
RandomForestOptions & | features_per_node (int p_features_per_node) |
The number of features that are considered when computing the split. More... | |
RandomForestOptions & | features_per_node (RandomForestOptionTags p_features_per_node_switch) |
The number of features that are considered when computing the split. More... | |
size_t | get_features_per_node (size_t total) const |
Get the actual number of features per node. More... | |
RandomForestOptions & | max_depth (size_t d) |
Do not split a node if its depth is greater or equal to max_depth. More... | |
RandomForestOptions & | min_num_instances (size_t n) |
Do not split a node if it contains less than min_num_instances data points. More... | |
RandomForestOptions & | n_threads (int n) |
The number of threads that are used in training. More... | |
RandomForestOptions & | node_complexity_tau (double tau) |
Value of the node complexity termination criterion. More... | |
RandomForestOptions & | resample_count (size_t n) |
If resample_count is greater than zero, the split in each node is computed using only resample_count data points. More... | |
RandomForestOptions & | split (RandomForestOptionTags p_split) |
The split criterion. More... | |
RandomForestOptions & | tree_count (int p_tree_count) |
The number of trees. More... | |
RandomForestOptions & | use_stratification (bool b) |
Use stratification when creating the bootstrap samples. More... | |
Options class for vigra::rf3::RandomForest version 3.
#include <vigra/random_forest_3.hxx>
Namespace: vigra::rf3
RandomForestOptions& tree_count | ( | int | p_tree_count | ) |
The number of trees.
Default: 255
RandomForestOptions& features_per_node | ( | int | p_features_per_node | ) |
The number of features that are considered when computing the split.
p_features_per_node | the number of features |
Default: use sqrt of the total number of features.
RandomForestOptions& features_per_node | ( | RandomForestOptionTags | p_features_per_node_switch | ) |
The number of features that are considered when computing the split.
p_features_per_node_switch | possible values: vigra::rf3::RF_SQRT (use square root of total number of features, recommended for classification), vigra::rf3::RF_LOG (use logarithm of total number of features, recommended for regression), vigra::rf3::RF_ALL (use all features). |
Default: vigra::rf3::RF_SQRT
RandomForestOptions& bootstrap_sampling | ( | bool | b | ) |
Use bootstrap sampling.
Default: true
RandomForestOptions& resample_count | ( | size_t | n | ) |
If resample_count is greater than zero, the split in each node is computed using only resample_count data points.
Default: n = 0 (don't resample in every node)
RandomForestOptions& split | ( | RandomForestOptionTags | p_split | ) |
The split criterion.
p_split | possible values: vigra::rf3::RF_GINI (use Gini criterion, vigra::rf3::GiniScorer), vigra::rf3::RF_ENTROPY (use entropy criterion, vigra::rf3::EntropyScorer), vigra::rf3::RF_KSD (use Kolmogorov-Smirnov criterion, vigra::rf3::KSDScorer). |
Default: vigra::rf3::RF_GINI
RandomForestOptions& max_depth | ( | size_t | d | ) |
Do not split a node if its depth is greater or equal to max_depth.
Default: d = 0 (don't use depth as a termination criterion)
RandomForestOptions& node_complexity_tau | ( | double | tau | ) |
Value of the node complexity termination criterion.
Default: tau = -1 (don't use complexity as a termination criterion)
RandomForestOptions& min_num_instances | ( | size_t | n | ) |
Do not split a node if it contains less than min_num_instances data points.
Default: n = 1 (don't use instance count as a termination criterion)
RandomForestOptions& use_stratification | ( | bool | b | ) |
Use stratification when creating the bootstrap samples.
That is, preserve the proportion between the number of class instances exactly rather than on average.
Default: false
RandomForestOptions& n_threads | ( | int | n | ) |
The number of threads that are used in training.
n = -1 means use number of cores, n = 0 means single-threaded training.
Default: n = -1 (use as many threads as there are cores in the machine).
RandomForestOptions& class_weights | ( | std::vector< double > const & | v | ) |
Each datapoint is weighted by its class weight. By default, each class has weight 1.
The classes in the random forest training have to follow a strict ordering. The weights must be given in that order. Example: You have the classes 3, 8 and 5 and use the vector {0.2, 0.3, 0.4} for the class weights. The ordering of the classes is 3, 5, 8, so class 3 will get weight 0.2, class 5 will get weight 0.3 and class 8 will get weight 0.4.
size_t get_features_per_node | ( | size_t | total | ) | const |
Get the actual number of features per node.
total | the total number of features |
This function is normally only called internally before training is started.
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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