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An introduction to AutoML
At the end we will receive an email and we can access the results (see Figure 41):
Figure 41: AutoML Vision – evaluating the results
When a particular problem includes an imbalanced dataset, accuracy isn't a
good metric to look for. For example, if your dataset contains 95 negatives and
5 positives, having a model with 95% accuracy doesn't make sense at all. The
classifier might label every example as negative and still achieve 95% accuracy.
Hence, we need to look for alternative metrics. Precision and Recall are very
good metrics to deal with such problems. It is also possible to access a detailed
evaluation by clicking the SEE FULL EVALUATION link and see the precision,
the Precision@1, and the Recall@1 (see Figure 42) together with the confusion
matrix (see Figure 43):
Figure 42: AutoML Vision – evaluating the results: Precision, Precision@1, Recall@1
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