# Amazon Machine Learning – Take Two

## Execution of Test

I performed the steps previously done using the new setup above. Without surprise the calculated AUC value returned was 1.0. For comparison I kept the score target to the default of 0.5.

I aggregated the results in this Excel sheet, which I have attached you for your reference.

AML-take2.xlsx (62.1 KiB, 355 hits)

## Results

Querying all 343 possible tuples, the model returned

• 335 correct predictions
• and 8 wrong predictions, which makes a 97.6% success rate or a 2.3% error rate.
• The wrong predictions are
• a = 2, b = 7, c = 3 (intermediate = 11, binary = true, prediction = false with a score of 418922)
• a = 3, b = 4, c = 2 (intermediate = 10, binary = false, prediction = true with a score of 618853)
• a = 3, b = 5, c = 5 (intermediate = 10, binary = false, prediction = true with a score of 543858)
• a = 4, b = 3, c = 2 (intermediate = 10, binary = false, prediction = true with a score of 808096)
• a = 4, b = 4, c = 6 (intermediate = 10, binary = false, prediction = true with a score of 696756)
• a = 4, b = 4, c = 7 (intermediate = 9, binary = false, prediction = true with a score of 534369)
• a = 5, b = 3, c = 5 (intermediate = 10, binary = false, prediction = true with a score of 529201)
• a = 7, b = 2, c = 4 (intermediate = 10, binary = false, prediction = true with a score of 55007)

It is noteworthy that out of the eight wrong predictions, seven of them is data which had been available to the training phase – and one of the four values is part of the test tuples (a = 7, b = 2, c = 4). Vice versa, this means that three out of four of our test tuples have been “guessed” correctly.

Also looking at the score of the false predictions there is a pattern to observe: Whilst the scores are very high for tuples which the model had seen during training (between 418,922 and 808,096 – the maximal score value in the entire data set is 999,999.9), the tuple which was not observed yet has a lower “confidence level” by a factor of 10.

Finally, looking at the intermediate values affected, you observe that only tuples with the intermediate values 9, 10 and 11 are subject to wrong predictions.

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