Trait opencv::hub_prelude::RTrees[−][src]

pub trait RTrees: DTrees + RTreesConst {
    fn as_raw_mut_RTrees(&mut self) -> *mut c_void;

    fn set_calculate_var_importance(&mut self, val: bool) -> Result<()> { ... }

    fn set_active_var_count(&mut self, val: i32) -> Result<()> { ... }

    fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()> { ... }
}

Required methods

Provided methods

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.

See also

setCalculateVarImportance getCalculateVarImportance

The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.

See also

setActiveVarCount getActiveVarCount

The termination criteria that specifies when the training algorithm stops. Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)

See also

setTermCriteria getTermCriteria

Implementations

Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.

Loads and creates a serialized RTree from a file

Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters
  • filepath: path to serialized RTree
  • nodeName: name of node containing the classifier
C++ default parameters
  • node_name: String()

Implementors