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 firsttosecondmatch 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.

Indicates that the classifier does not yet have all relevant model data, so a subsequent attempt to classify the same sample again may produce new results with higher accuracy.
Note
Classifiers manage the fetching and caching of model data internally, so if the classifier returns results flagged withmoreComing
it will already have requested the missing model data from the server. Provided a working internet connection is available, the missing model data should be available in the classifier in less than a second.Declaration
Swift
public let moreComing: Bool

Returns the result rows as a plain array.
Declaration
Swift
public var array: [ClassifierResultItem]

A convenience subscript to enable lookup by
ActivityTypeName
.let walkingResult = results[.walking]
Declaration
Swift
public subscript(activityType: ActivityTypeName) > ClassifierResultItem?

Declaration
Swift
public mutating func next() > ClassifierResultItem?