Which visualization will accomplish this?
A histogram showing whether the most important input feature is Gaussian.
A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
A scatter plot showing the performance of the objective metric over each training iteration.
A scatter plot showing the correlation between maximum tree depth and the objective metric.
Explanations:
Visualizing the distribution of the most important feature does not provide information about hyperparameter tuning ranges or impact on the objective metric (AUC).
t-SNE visualization helps in dimensionality reduction but does not directly relate to tuning hyperparameters for improving AUC performance or reducing training time.
Tracking the objective metric over iterations shows model convergence but does not assist in reconfiguring the input hyperparameter ranges to decrease training time.
Showing the correlation between maximum tree depth and the objective metric can reveal if certain depth values yield optimal AUC, guiding adjustments to the hyperparameter range for improved efficiency.
I rank that the answer is:
A scatter plot showing the correlation between maximum tree depth and the objective metric.