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/lib/mlbackend/php/phpml/src/Phpml/Classification/Ensemble/ -> RandomForest.php (source)

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Defines 1 class

RandomForest:: (6 methods):
  __construct()
  setFeatureSubsetRatio()
  setClassifer()
  getFeatureImportances()
  setColumnNames()
  initSingleClassifier()


Class: RandomForest  - X-Ref

__construct(int $numClassifier = 50)   X-Ref
Initializes RandomForest with the given number of trees. More trees
may increase the prediction performance while it will also substantially
increase the processing time and the required memory


setFeatureSubsetRatio($ratio)   X-Ref
This method is used to determine how many of the original columns (features)
will be used to construct subsets to train base classifiers.<br>

Allowed values: 'sqrt', 'log' or any float number between 0.1 and 1.0 <br>

Default value for the ratio is 'log' which results in log(numFeatures, 2) + 1
features to be taken into consideration while selecting subspace of features

param: string|float $ratio

setClassifer(string $classifier, array $classifierOptions = [])   X-Ref
RandomForest algorithm is usable *only* with DecisionTree

return: $this

getFeatureImportances()   X-Ref
This will return an array including an importance value for
each column in the given dataset. Importance values for a column
is the average importance of that column in all trees in the forest


setColumnNames(array $names)   X-Ref
A string array to represent the columns is given. They are useful
when trying to print some information about the trees such as feature importances

return: $this

initSingleClassifier(Classifier $classifier)   X-Ref

param: DecisionTree $classifier
return: DecisionTree