Trait opencv::prelude::DTreesConst

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pub trait DTreesConst: StatModelConst {
Show 14 methods
    fn as_raw_DTrees(&self) -> *const c_void;

    fn get_max_categories(&self) -> Result<i32> { ... }
    fn get_max_depth(&self) -> Result<i32> { ... }
    fn get_min_sample_count(&self) -> Result<i32> { ... }
    fn get_cv_folds(&self) -> Result<i32> { ... }
    fn get_use_surrogates(&self) -> Result<bool> { ... }
    fn get_use1_se_rule(&self) -> Result<bool> { ... }
    fn get_truncate_pruned_tree(&self) -> Result<bool> { ... }
    fn get_regression_accuracy(&self) -> Result<f32> { ... }
    fn get_priors(&self) -> Result<Mat> { ... }
    fn get_roots(&self) -> Result<Vector<i32>> { ... }
    fn get_nodes(&self) -> Result<Vector<DTrees_Node>> { ... }
    fn get_splits(&self) -> Result<Vector<DTrees_Split>> { ... }
    fn get_subsets(&self) -> Result<Vector<i32>> { ... }
}

Expand description

The class represents a single decision tree or a collection of decision trees.

The current public interface of the class allows user to train only a single decision tree, however the class is capable of storing multiple decision trees and using them for prediction (by summing responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this capability to implement decision tree ensembles.

Required Methods§

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fn as_raw_DTrees(&self) -> *const c_void

Provided Methods§

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fn get_max_categories(&self) -> Result<i32>

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than maxCategories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including our implementation) try to find sub-optimal split in this case by clustering all the samples into maxCategories clusters that is some categories are merged together. The clustering is applied only in n > 2-class classification problems for categorical variables with N > max_categories possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases. Default value is 10.

fn get_max_depth(&self) -> Result<i32>

The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth. The actual depth may be smaller if the other termination criteria are met (see the outline of the training procedure @ref ml_intro_trees “here”), and/or if the tree is pruned. Default value is INT_MAX.

fn get_min_sample_count(&self) -> Result<i32>

If the number of samples in a node is less than this parameter then the node will not be split.

Default value is 10.

fn get_cv_folds(&self) -> Result<i32>

If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. Default value is 10.

fn get_use_surrogates(&self) -> Result<bool>

If true then surrogate splits will be built. These splits allow to work with missing data and compute variable importance correctly. Default value is false.

Note: currently it’s not implemented.

fn get_use1_se_rule(&self) -> Result<bool>

If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. Default value is true.

fn get_truncate_pruned_tree(&self) -> Result<bool>

If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree. Default value is true.

fn get_regression_accuracy(&self) -> Result<f32>

Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further. Default value is 0.01f

fn get_priors(&self) -> Result<Mat>

The array of a priori class probabilities, sorted by the class label value.

The parameter can be used to tune the decision tree preferences toward a certain class. For example, if you want to detect some rare anomaly occurrence, the training base will likely contain much more normal cases than anomalies, so a very good classification performance will be achieved just by considering every case as normal. To avoid this, the priors can be specified, where the anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is adjusted properly.

You can also think about this parameter as weights of prediction categories which determine relative weights that you give to misclassification. That is, if the weight of the first category is 1 and the weight of the second category is 10, then each mistake in predicting the second category is equivalent to making 10 mistakes in predicting the first category. Default value is empty Mat.