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Developer Documentation
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| File Size: | 252 lines (7 kb) |
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| Includes or requires: | 0 files |
Defines 1 class
AdaBoost:: (9 methods):
__construct()
setBaseClassifier()
train()
predictSample()
getBestClassifier()
resample()
evaluateClassifier()
calculateAlpha()
updateWeights()
| __construct(int $maxIterations = 50) X-Ref |
| ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to improve classification performance of 'weak' classifiers such as DecisionStump (default base classifier of AdaBoost). |
| setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = []) X-Ref |
| Sets the base classifier that will be used for boosting (default = DecisionStump) |
| train(array $samples, array $targets) X-Ref |
| predictSample(array $sample) X-Ref |
return: mixed |
| getBestClassifier() X-Ref |
| Returns the classifier with the lowest error rate with the consideration of current sample weights |
| resample() X-Ref |
| Resamples the dataset in accordance with the weights and returns the new dataset |
| evaluateClassifier(Classifier $classifier) X-Ref |
| Evaluates the classifier and returns the classification error rate |
| calculateAlpha(float $errorRate) X-Ref |
| Calculates alpha of a classifier |
| updateWeights(Classifier $classifier, float $alpha) X-Ref |
| Updates the sample weights |
