# Child's Target Height Prediction Evolution
## 4 Work Methodology Proposed
### Modelling
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean Squared Error** & **Accuracy** \\ \hline Linear & 5.1781 & 60.33\% \\ SVR RBF & 6.1269 & 53.08\% \\ SVR Linear & 5.2167 & 60.04\% \\ SVR Poly & Invalid output & — \\ SVR & 13.262 & 1.58\% \\ Sigmoid & 9.8600 & 24.47\% \\ Elastic Net & 9.9896 & 23.48\% \\ Ridge & 5.1829 & 60.30\% \\ KNeighbors & 6.3785 & 51.14\% \\ Bayesian & 5.1805 & 60.32\% \\ AdaBoost & 5.3941 & 58.68\% \\ SGD & Invalid output & — \\ Decision & 5.5017 & 57.86\% \\ Tree & 4.8290 & 63.01\% \\ XGB & 4.7938 & 63.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Machine learning algorithms performance with default parameter. Although, this technique improved the accuracy of some algorithms, an improvement was not made on the height prediction parameter. Note, in this case, SVR Poly and SGD were able to converge and present a valid output, as the new scale overcomes the previous verified convergence issue with these two algorithms. For hyperparameters tuning we considered two possible strategies: Grid search and Random Search. As Grid search requires a lot of resources and time to compute all possible algorithms and parameter dimensional space and those that adopted the random search strategy performed several iterations. The applicability of this technique to the study dataset can be observed in Table 8.  
Hyper-parameter tuning has improved the accuracy of many algorithms, and most importantly, has achieved progress on the study goal, increasing children's \"target\" height prediction accuracy. Besides the importance of the performance output, it is also important to compare the complexities and costs regarding the algorithm's applicability. We have decided to perform an analysis of the computation resources consumption required for each of the methods used above. Since CPU consumption can present some variability, we decided to analyse CPU time mean and standard deviation by considering 1000 executions (Table 9). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean 2 Error** & **Accuracy** & **Impact** \\ \hline Linear & 5.1781 & 60.33\% & No change \\ SVR RBF & 5.3131 & 59.30\% & Improved \\ SVR Linear & 5.2398 & 59.86\% & Deteriorate \\ SVR Poly & 5.7943 & 55.61\% & Valid output \\ SVR Sigmoid & 5.4715 & 58.09\% & Improved \\ Lasso & 10.6798 & 18.19\% & Deteriorate \\ Elastic Net & 10.8461 & 16.92\% & Deteriorate \\ Ridge & 5.1916 & 60.23\% & Deteriorate \\ KNeighbors & 5.4779 & 58.03\% & Improved \\ Bayesian & 5.1798 & 60.32\% & No change \\ AdaBoost & 5.2607 & 59.70\% & Improved \\ SGD & 17.3546 & 32.93\% & Valid output \\ Decision Tree & 5.5299 & 57.64\% & No change \\ XGB & 4.8290 & 63.01\% & No change \\ LightGBM & 4.7938 & 63.28\% & No change \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Machine learning algorithms performance with MaxAbsScaler. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean 2 Error** & **Accuracy** & **Impact** \\ \hline Linear & 5.1781 & 60.33\% & No change \\ SVR RBF & 5.3131 & 59.30\% & No change \\ SVR Linear & 5.2021 & 60.15\% & Improved \\ SVR Poly & 5.2965 & 59.42\% & Improved \\ SVR Sigmoid & 5.3044 & 59.36\% & Improved \\ Lasso & 5.1697 & 60.40\% & Improved \\ Elastic Net & 5.1697 & 60.39\% & Improved \\ Ridge & 5.1782 & 60.33\% & Improved \\ KNeighbors & 5.2945 & 59.44\% & Improved \\ Bayesian & 5.1798 & 60.32\% & No change \\ AdaBoost & 5.2607 & 59.70\% & No change \\ SGD & 5.1871 & 60.27\% & Improved \\ Decision Tree & 5.5339 & 57.60\% & No change \\ XGB & 4.7125 & 63.90\% & Improved \\ LightGBM & 4.6159 & 64.64\% & Improved \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Machine learning algorithms performance after hyper-parameters tuning. The computer supporting this study has the following characteristics:  
* Processor: Intel Core i7-3770 3.40 GHz. * RAM: 8 GB. * Operating System: Windows 10, 64 bits, x64-based processor.