Search moodle.org's
Developer Documentation
Developer Documentation
See Release Notes
Long Term Support Release
- Bug fixes for general core bugs in 4.1.x will end 13 November 2023 (12 months).
- Bug fixes for security issues in 4.1.x will end 10 November 2025 (36 months).
- PHP version: minimum PHP 7.4.0 Note: minimum PHP version has increased since Moodle 4.0. PHP 8.0.x is supported too.
(no description)
| File Size: | 252 lines (7 kb) |
| Included or required: | 0 times |
| Referenced: | 0 times |
| 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 |
