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Macro F1 is a macro-averaged F1 score aiming at a balanced performance measurement. To calculate macro F1, two different averaging-formulas have been used: the F1 score of (arithmetic) class-wise precision and recall means or the arithmetic mean of class-wise F1 scores, where the latter exhibits more desirable properties. [28]
Formula One, abbreviated to F1, is the highest class of open-wheeled auto racing series administered by the Fédération Internationale de l'Automobile (FIA), motorsport's world governing body. [1] The "formula" in the name alludes to a series of rules set by the FIA to which all participants and vehicles are required to conform.
To calculate the recall for a given class, we divide the number of true positives by the prevalence of this class (number of times that the class occurs in the data sample). The class-wise precision and recall values can then be combined into an overall multi-class evaluation score, e.g., using the macro F1 metric. [21]
An F-score is a combination of the precision and the recall, providing a single score. There is a one-parameter family of statistics, with parameter β, which determines the relative weights of precision and recall. The traditional or balanced F-score is the harmonic mean of precision and recall:
In computer science, specifically information retrieval and machine learning, the harmonic mean of the precision (true positives per predicted positive) and the recall (true positives per real positive) is often used as an aggregated performance score for the evaluation of algorithms and systems: the F-score (or F-measure).
This is a list of points scoring systems used to determine the outcome of the seasonal motorsports championships. Points are usually awarded depending on placement in the individual races.
The World Constructors' Championship is presented by the FIA to the most successful F1 constructor over the course of the season through a points system based on individual Grand Prix results. Constructors' Championship points are calculated by adding points scored in each race by any driver for that constructor. [4]
Note that the F1 score depends on which class is defined as the positive class. In the first example above, the F1 score is high because the majority class is defined as the positive class. Inverting the positive and negative classes results in the following confusion matrix: TP = 0, FP = 0; TN = 5, FN = 95. This gives an F1 score = 0%.
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