MJRoBot (Marcelo Rovai) / XIAOML Kit Motion Class - AD 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	Anomaly	Accuracy	Result
testing.60r6m29c	testing	10s	-0.15		41 terrestrial	Show classification Show in data acquisition Show in feature explorer
testing.60r6fpa9	testing	10s	-0.09		36 lift, 3 uncertain, 2 maritime	Show classification Show in data acquisition Show in feature explorer
terrestrial.60r3bphu	terrestrial	10s	0.04	98%	40 terrestrial, anomaly	Show classification Show in data acquisition Show in feature explorer
terrestrial.60r36r33	terrestrial	10s	-0.15	100%	41 terrestrial	Show classification Show in data acquisition Show in feature explorer
lift.60r3hrhj	lift	10s	-0.13	100%	41 lift	Show classification Show in data acquisition Show in feature explorer
lift.60r3jdeo	lift	10s	-0.08	100%	41 lift	Show classification Show in data acquisition Show in feature explorer
maritime.60r3l6ra	maritime	10s	-0.01	98%	40 maritime, anomaly	Show classification Show in data acquisition Show in feature explorer
maritime.60r3pb88	maritime	10s	-0.09	100%	41 maritime	Show classification Show in data acquisition Show in feature explorer
idle.60r3srim	idle	10s	-0.12	100%	41 idle	Show classification Show in data acquisition Show in feature explorer
idle.60r42qjl	idle	10s	-0.08	100%	41 idle	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

99.39%

Metrics for Classifier

Metric	Value
Area under ROC Curve	1.00
Weighted average Precision	1.00
Weighted average Recall	1.00
Weighted average F1 score	1.00

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

Anomaly detection

Minimum score before tagging as anomaly

Should be numeric

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	MJRoBot (Marcelo Rovai)

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