ClassifierResults

public struct ClassifierResults: Sequence, IteratorProtocol

The results of a call to classify(_:types:) on an ActivityTypeClassifier.

Classifier Results are an iterable sequence of ClassifierResultItem rows, with each row representing a single ActivityTypeName and its match probability score.

The results are ordered from best match to worst match, thus the first result row represents the best match for the given sample.

Using The Results

The simplest way to use the results is to take the first result row (ie the best match) and ignore the rest.

let results = classifier.classify(sample)

let bestMatch = results.first

You could also iterate through the results, in order from best match to worst match.

for result in results {
    print("name: \(result.name) score: \(result.score)")
}

If you want to know the probability score of a specific type, you could extract that result row by ActivityTypeName:

let walkingResult = results[.walking]

If you want the first and second result rows:

let firstResult = results[0]
let secondResult = results[1]

Interpreting Classifier Results

Two key indicators can help to interpret the probability scores. The first being the most obvious: a higher score indicates a better match.

The second, and perhaps more important indicator, is the ratio of the best match’s score to the second best match’s score.

For example if the first result row has a probability score of 0.9 (a 90% match) while the second result row’s score is 0.1 (a 10% match), that indicates that the best match is nine times more probable than the second best match (0.9 / 0.1 = 9.0). However if the second row’s score where instead 0.8, the first row would only be 1.125 times more probable than the second (0.9 / 0.8 = 1.125).

The ratio between the first and second best matches can be loosely considered a confidence score. Thus the 0.9 / 0.1 = 9.0 example gives a confidence score of 9.0, whilst the second example of 0.9 / 0.8 = 1.125 gives a much lower confidence score of 1.125.

A real world example might be results that have car and bus as the top two results. If both types achieve a high probability score, but the scores are close together, that indicates there is high confidence that the type is either car or bus, but low confidence of knowing which one of the two it is.

The easiest way to apply these two metrics is with simple thresholds. For example a raw score threshold of 0.01 and a first-to-second-match ratio threshold of 2.0. If the first match falls below these thresholds, you could consider it an uncertain match. Although which kinds of thresholds to use will depend heavily on the application.