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An introduction to AutoML
Clicking on the suggested URL, it is possible to see the results of our training. The
AutoML generated model reached an accuracy of 90% (see Figure 15). Remember
that accuracy is the fraction of classification predictions produced by the model that
were correct on a test set, which is held automatically. The log-loss (for example, the
cross-entropy between the model predictions and the label values) is also provided.
A lower value indicates a higher-quality model.
In addition, the Area Under the Cover Receiver Operating Characteristic (AUC
ROC) curve is represented. This ranges from zero to one, and a higher value
indicates a higher-quality model. This statistic summarizes a AUC ROC curve, which
is a graph showing the performance of a classification model at all classification
TTTT
thresholds. The True Positive Rate (TPR) (also known as "recall") is: TTTTTT =
TTTT + FFFF
where TP is the number of true positives and FN is the number of false negatives.
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The False Positive Rate (FPR) is: FFFFFF = , where FP is the number of false
FFFF + TTTT
positives and TN is the number of true negatives.
A ROC curve plots TPR versus FPR at different classification thresholds. In Figure
15 you will see the Area Under the Curve (AUC) for one threshold of a ROC curve,
whereas you can see the ROC curve itself in Figure 17.
It is possible to deep dive into the evaluation by accessing the evaluation tab and see
additional information (see Figure 16) and access the confusion matrix (see Figure 17):
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