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Fbeta is the weighted harmonic mean between precision and recall When the Fbeta score of your machine learning model is closer to 1 it means that the model is trained well and if it is closer to 0 it means that the model is not well trained When using it to evaluate the performance of a machine learning model you should know that if you set the beta value.
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The Fbeta score calculation follows the same form as the F1 score however it also allows you to decide how to weight the balance between precision and recall using the beta parameter The F.
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The Fbeta score is the weighted harmonic mean of precision and recall reaching its optimal value at 1 and its worst value at 0 The beta parameter determines the weight of recall in the combined score beta < 1 lends more weight to precision while beta > 1 favors recall (beta> 0 considers only precision beta> +inf only recall) Read more in the User Guide Parameters.
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Beta is a measure of the volatility or systematic risk of a security or a portfolio in comparison to the market as a whole Beta is used in the.
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