Whenever we come across an imbalanced class problem, the metric to measure is often F1 score and not accuracy. A quick reminder that the F1 score is the harmonic mean of precision and recall.
Precision is how accurate is your ML model in its predictions.
Recall is a measure of the model’s ability to correctly identify the positive class.
So the F1 score is a balanced measure of both recall and precision. But what if you want to prioritize reducing false positives or reducing false negatives, there comes F-beta. It’s a generalized metric, where a parameter beta is introduced to generalize the F-score.
*precision*recall}{\beta^{2}*precision&space;+&space;recall}&space; "https://latex.codecogs.com/svg.image?F_{\beta} = \frac{(1+\beta^{2})*precision*recall}{\beta^{2}*precision + recall}")
This enables one to choose an appropriate beta value to tune for the task at hand. If you want to minimize false positives, you want to increase the weight of precisions, so you should choose a value of beta less than 1, typically 0.5 is chosen and is called F0.5 score.
Similarly, if you want to increase the importance of recall and reduce false negatives, you should choose a value of beta greater than 1, typically 2 is selected and is called F2 score.
In a nutshell, you should optimize F2 score to reduce false negatives and F0.5 score to reduce false positives.
ML EXPLAINED 
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