ml_RTrees
import { ml_RTrees } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits ml_DTrees.
Random Trees Classifier *
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The class implements the random forest predictor.
See: ml_intro_rtrees
Constructors and members
static create
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.
create(): ml_RTrees | null;The ml_RTrees | null result.
static load
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
load(filepath: EmbindString, nodeName: EmbindString): ml_RTrees | null;2 available overloads
load(filepath: EmbindString): ml_RTrees | null;load(filepath: EmbindString, nodeName: EmbindString): ml_RTrees | null;filepathpath to serialized RTree
nodeNamename of node containing the classifier
The ml_RTrees | null result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
getCalculateVarImportance
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false. See: setCalculateVarImportance
getCalculateVarImportance(): boolean;The boolean result.
setCalculateVarImportance
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false. See: setCalculateVarImportance See: getCalculateVarImportance
setCalculateVarImportance(val: boolean): void;valval argument (boolean).
getActiveVarCount
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: setActiveVarCount
getActiveVarCount(): number;The number result.
setActiveVarCount
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: setActiveVarCount See: getActiveVarCount
setActiveVarCount(val: number): void;valval argument (number).
getTermCriteria
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: setTermCriteria
getTermCriteria(): TermCriteria;The TermCriteria result.
setTermCriteria
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: setTermCriteria See: getTermCriteria
setTermCriteria(val: TermCriteria): void;valval argument (TermCriteria).
getVarImportance
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
getVarImportance(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
getVotes
Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
getVotes(samples: Mat, results: Mat, flags: number): void;samplesArray containing the samples for which votes will be calculated.
resultsOutput destination, filled by the native operation. Array where the result of the calculation will be written.
flagsFlags for defining the type of RTrees.
These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.