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67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
# Classification Report
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Class for calculate main classifier metrics: precision, recall, F1 score and support.
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### Report
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To generate report you must provide the following parameters:
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* $actualLabels - (array) true sample labels
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* $predictedLabels - (array) predicted labels (e.x. from test group)
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```
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use Phpml\Metric\ClassificationReport;
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$actualLabels = ['cat', 'ant', 'bird', 'bird', 'bird'];
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$predictedLabels = ['cat', 'cat', 'bird', 'bird', 'ant'];
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$report = new ClassificationReport($actualLabels, $predictedLabels);
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```
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Optionally you can provide the following parameter:
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* $average - (int) averaging method for multi-class classification
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* `ClassificationReport::MICRO_AVERAGE` = 1
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* `ClassificationReport::MACRO_AVERAGE` = 2 (default)
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* `ClassificationReport::WEIGHTED_AVERAGE` = 3
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### Metrics
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After creating the report you can draw its individual metrics:
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* precision (`getPrecision()`) - fraction of retrieved instances that are relevant
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* recall (`getRecall()`) - fraction of relevant instances that are retrieved
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* F1 score (`getF1score()`) - measure of a test's accuracy
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* support (`getSupport()`) - count of testes samples
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```
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$precision = $report->getPrecision();
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// $precision = ['cat' => 0.5, 'ant' => 0.0, 'bird' => 1.0];
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```
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### Example
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```
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use Phpml\Metric\ClassificationReport;
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$actualLabels = ['cat', 'ant', 'bird', 'bird', 'bird'];
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$predictedLabels = ['cat', 'cat', 'bird', 'bird', 'ant'];
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$report = new ClassificationReport($actualLabels, $predictedLabels);
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$report->getPrecision();
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// ['cat' => 0.5, 'ant' => 0.0, 'bird' => 1.0]
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$report->getRecall();
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// ['cat' => 1.0, 'ant' => 0.0, 'bird' => 0.67]
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$report->getF1score();
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// ['cat' => 0.67, 'ant' => 0.0, 'bird' => 0.80]
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$report->getSupport();
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// ['cat' => 1, 'ant' => 1, 'bird' => 3]
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$report->getAverage();
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// ['precision' => 0.5, 'recall' => 0.56, 'f1score' => 0.49]
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```
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