A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model.

When using theHyperoptlibrary withfmin, the goal is to find the minimum of the objective function. Since you are usingcross_val_scoreto calculate the R2 score which is a measure of the proportion of the variance for a dependent variable that's explained by an independent variable(s) in a regression model, higher values are better. However,fminseeks to minimize the objective function, so to align withfmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2,fminis effectively maximizing the R2 score, which can lead to a more accurate model.

References

Hyperopt Documentation: http://hyperopt.github.io/hyperopt/

Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html