Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library’s fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.

They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?
A . Add test set validation process
B . Add a random_state argument to the RandomForestRegressor operation
C . Remove the mean operation that is wrapping the cross_val_score operation
D . Replace the r2 return value with -r2
E . Replace the fmin operation with the fmax operation

Answer: D

Explanation:

When using the Hyperopt library with fmin, the goal is to find the minimum of the objective function. Since you are using cross_val_score to 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, fmin seeks to minimize the objective function, so to align with fmin’s goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2, fmin is effectively maximizing the R2 score, which can lead to a more accurate model.

Reference

Hyperopt Documentation: http://hyperopt.github.io/hyperopt/ Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html