Shebin Jose Jacob / AI Cart Public
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Test data

Set confidence thresholds
Model variants
No additional model variants available Test 'Unoptimized (float32)' model
Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.
Sample name Expected outcome Length Precision score Result
Popcorn Popcorn 0s 0%
Show classification Show in data acquisition
Popcorn Popcorn 0s 80%
Show classification Show in data acquisition
Popcorn Popcorn 0s 90%
Show classification Show in data acquisition
Popcorn Popcorn 0s 90%
Show classification Show in data acquisition
Popcorn Popcorn 0s 90%
Show classification Show in data acquisition
Popcorn Popcorn 0s 99%
Show classification Show in data acquisition
Popcorn Popcorn 0s 99%
Show classification Show in data acquisition
Popcorn Popcorn 0s 90%
Show classification Show in data acquisition
Popcorn Popcorn 0s 90%
Show classification Show in data acquisition
Coke Coke 0s 90%
Show classification Show in data acquisition
Coke Coke 0s 99%
Show classification Show in data acquisition
Coke Coke 0s 99%
Show classification Show in data acquisition
Coke Coke 0s 0%
Show classification Show in data acquisition
Coke Coke 0s 99%
Show classification Show in data acquisition
Coke Coke 0s 80%
Show classification Show in data acquisition
Coke Coke 0s 80%
Show classification Show in data acquisition
Coke Coke 0s 99%
Show classification Show in data acquisition
Coke Coke 0s 90%
Show classification Show in data acquisition
Coke Coke 0s 80%
Show classification Show in data acquisition
Lays Lays 0s 0%
Show classification Show in data acquisition
Lays Lays 0s 90%
Show classification Show in data acquisition
Lays Lays 0s 0%
Show classification Show in data acquisition
Lays Lays 0s 90%
Show classification Show in data acquisition
Lays Lays 0s 90%
Show classification Show in data acquisition
Lays Lays 0s 99%
Show classification Show in data acquisition
Lays Lays 0s 90%
Show classification Show in data acquisition
Lays Lays 0s 80%
Show classification Show in data acquisition
Lays Lays 0s 99%
Show classification Show in data acquisition

Model testing output

Results

Model version:
Accuracy
85.71%

Confusion matrix

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Feature explorer

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