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Developer Documentation
- Bug fixes for general core bugs in 4.0.x will end 8 May 2023 (12 months).
- Bug fixes for security issues in 4.0.x will end 13 November 2023 (18 months).
- PHP version: minimum PHP 7.3.0 Note: the minimum PHP version has increased since Moodle 3.10. PHP 7.4.x is also supported.
Differences Between: [Versions 400 and 402] [Versions 400 and 403]
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| File Size: | 319 lines (10 kb) |
| Included or required: | 0 times |
| Referenced: | 0 times |
| Includes or requires: | 0 files |
Defines 1 class
DecisionStump:: (10 methods):
__construct()
__toString()
setNumericalSplitCount()
trainBinary()
getBestNumericalSplit()
getBestNominalSplit()
calculateErrorRate()
predictProbability()
predictSampleBinary()
resetBinary()
Class: DecisionStump - X-Ref
| __construct(int $columnIndex = self::AUTO_SELECT) X-Ref |
| A DecisionStump classifier is a one-level deep DecisionTree. It is generally used with ensemble algorithms as in the weak classifier role. <br> If columnIndex is given, then the stump tries to produce a decision node on this column, otherwise in cases given the value of -1, the stump itself decides which column to take for the decision (Default DecisionTree behaviour) |
| __toString() X-Ref |
| No description |
| setNumericalSplitCount(float $count) X-Ref |
| While finding best split point for a numerical valued column, DecisionStump looks for equally distanced values between minimum and maximum values in the column. Given <i>$count</i> value determines how many split points to be probed. The more split counts, the better performance but worse processing time (Default value is 10.0) |
| trainBinary(array $samples, array $targets, array $labels) X-Ref |
| getBestNumericalSplit(array $samples, array $targets, int $col) X-Ref |
| Determines best split point for the given column |
| getBestNominalSplit(array $samples, array $targets, int $col) X-Ref |
| No description |
| calculateErrorRate(array $targets, float $threshold, string $operator, array $values) X-Ref |
| Calculates the ratio of wrong predictions based on the new threshold value given as the parameter |
| predictProbability(array $sample, $label) X-Ref |
| Returns the probability of the sample of belonging to the given label Probability of a sample is calculated as the proportion of the label within the labels of the training samples in the decision node param: mixed $label |
| predictSampleBinary(array $sample) X-Ref |
return: mixed |
| resetBinary() X-Ref |
| No description |
