What technique is used to improve the performance of a model by balancing the trade-off between precision and recall?

The technique of optimizing the F1 Score is crucial in situations where there is a need to balance precision and recall. The F1 Score is defined as the harmonic mean of precision and recall, making it a useful metric when the classes are imbalanced, or when false negatives and false positives carry different weights in a problem. By focusing on maximizing the F1 Score, you are effectively promoting a model that performs well on both measures—precision (the rate of true positives among all positive predictions) and recall (the rate of true positives among all actual positives).

Using the F1 Score as an optimization target allows for a single metric that encapsulates the balance between these two important aspects of model performance. This is particularly valuable in applications like fraud detection or medical diagnostics, where both false positives and false negatives can have critical consequences.

While hyperparameter tuning and grid search are important for optimizing a model's parameters, they do not inherently focus on balancing precision and recall. Feature engineering helps create better inputs for the model but doesn't directly address the trade-off between precision and recall. Therefore, F1 Score optimization stands out as the most relevant technique in this context.