# Hyperparameter Optimization Using Successive Halving with Greedy Cross Validation
## 4 Discussion  
### Summary and Discussion of Findings  
The results of the experiments described in the previous section indicate (1) that the greedy successive halving algorithm was significantly faster than the standard successive halving algorithm in 100% of the experimental conditions considered in the study; and (2) that the greedy successive halving algorithm was able to select final ML models whose quality was statistically identical to the models chosen by standard successive halving in approximately 98.3% of those conditions. Since greedy successive halving was observed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **ML** & **Greedy Successive Halving** & \multicolumn{2}{c}{**Standard Successive Halving**} \\  & **Algorithm** & \(k\) **= 5** & \(k\) **= 10** & \(k\) **= 5** & \(k\) **= 10** \\ \hline \multirow{3}{*}{California Housing} & Passive & \multirow{3}{*}{1.021} & \multirow{3}{*}{1.017} & \multirow{3}{*}{1.020} & \multirow{3}{*}{1.007} \\  & Aggressive & & & & \\  & Tweedie & & & & \\ \hline \multirow{3}{*}{Diabetes} & Passive & \multirow{3}{*}{1.050} & \multirow{3}{*}{1.099} & \multirow{3}{*}{1.031} & \multirow{3}{*}{1.042} \\  & Aggressive & & & & \\  & Tweedie & & & & \\ \hline \multirow{3}{*}{Wine Recognition} & [PERSON] & \multirow{3}{*}{0.961} & \multirow{3}{*}{0.968} & \multirow{3}{*}{0.974} & \multirow{3}{*}{0.976} \\  & Naive Bayes & & & & \\ \cline{1-1}  & Decision Tree & & & & \\ \cline{1-1}  & Deep Neural & & & & \\ \cline{1-1}  & Network & & & & \\ \hline \multirow{3}{*}{Wisconsin Diagnostic Breast Cancer} & Bernoulli & \multirow{3}{*}{0.992} & \multirow{3}{*}{0.991} & \multirow{3}{*}{0.993} & \multirow{3}{*}{0.992} \\  & Naive Bayes & & & & \\ \cline{1-1}  & Decision Tree & & & & \\ \cline{1-1}  & Deep Neural & & & & \\ \cline{1-1}  & Network & & & & \\ \hline \end{tabular} \({}^{st}\)_p_ < 0.01, for a [PERSON]’s t-test comparing the quality of the final ML models chosen by the greedy and standard successive halving algorithms.  
\end{table} 
Table 8: Mean standardized quality of final ML models chosen from a set of 1000 candidate models, relative to ground truth optimal model. to always be much faster than standard successive halving, and since greedy successive halving almost always chose final models of the same quality as standard successive halving, it can be reasonably concluded that the proposed greedy successive halving algorithm is generally superior to the standard successive halving algorithm in terms of its ability to perform hyperparameter optimization for AI/ML scenarios. While the results reported in the previous section provide insights into the comparative performance of greedy successive halving in specific scenarios, some consideration and discussion of the proposed algorithm's average level of performance in comparison to standard successive halving may be instructive. To this end, across all 60 of the experimental conditions evaluated in the study, greedy successive halving was, on average, 3.59 times faster than standard successive halving when performing identical ML model selection tasks, with greedy successive halving's advantage in speed ranging from a minimum of 2.65 times faster to a maximum of 5.42 times faster than standard successive halving. This remarkable difference in the speed with which greedy successive halving can complete the hyperparameter optimization process has very significant implications for AI/ML practitioners. For example, given a desire to complete the hyperparameter optimization process as quickly and inexpensively as possible, and assuming the use of identical hardware resources, AI/ML practitioners relying on greedy successive halving would be able to evaluate the same number of candidate ML models 3.59 times more quickly than if they had chosen to use standard successive halving to perform the same task.