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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eCalculates the Fβ-score, a measure combining precision and recall, from a confusion matrix.\u003c/p\u003e\n"],["\u003cp\u003eAllows customization of the score through a \u003ccode\u003ebeta\u003c/code\u003e parameter, weighing the importance of recall relative to precision.\u003c/p\u003e\n"],["\u003cp\u003eProvides flexibility by accepting the confusion matrix as input and returning the Fβ-score as an array.\u003c/p\u003e\n"]]],["The `fscore` function calculates the Fβ-score for a given confusion matrix. It accepts a `beta` argument, a float value defaulting to 1, which determines the relative importance of recall versus precision. A higher `beta` emphasizes recall. The function operates on a `ConfusionMatrix` object and returns an array containing the computed Fβ-score. The standard F-score is calculated when β is set to one.\n"],null,[]]