glamba / Light intensity Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name	Expected outcome	Length	Accuracy	Result
viable.VO3_1U	viable	1461d 0s	34%	674 not_viable, 499 viable, 288 uncertain	Show classification Show in data acquisition Show in feature explorer
viable.VO3_3U	viable	1461d 0s	40%	585 not_viable, 582 viable, 294 uncertain	Show classification Show in data acquisition Show in feature explorer
not_viable.VO1_2U	not_viable	1461d 0s	85%	1248 not_viable, 191 uncertain, 22 viable	Show classification Show in data acquisition Show in feature explorer
viable.VO5_1U	viable	1461d 0s	79%	1158 viable, 183 not_viable, 120 uncertain	Show classification Show in data acquisition Show in feature explorer
viable.VO5_4U	viable	1461d 0s	72%	1053 viable, 222 not_viable, 186 uncertain	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

62.15%

Metrics for Classifier

Metric	Value
Area under ROC Curve	0.79
Weighted average Precision	0.87
Weighted average Recall	0.68
Weighted average F1 score	0.72

Confusion matrix

Feature explorer

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Classifier

Minimum confidence rating

Should be numeric

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	Name
	s s2wen
	g glamba
	m matthew.iraa
	A Arhana

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Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

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Test data

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