## 4 Results
### The Arduino Implementation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Regression** & **R Score** & **R\({}^{2}\) Score** & **MSE** & **EVS** & **Max. Err** & **MAE** & **MSL** & **MAP** & **MedAE** \\ \hline Bayes R & 0.551882 & 0.371885 & 0.107154 & 0.413885 & 1.419959 & 0.190234 & 0.003582 & 0.038744 & 0.066796 \\ ARD & \(-\)0.00198 & \(-\)0.03552 & 0.176656 & \(-\)2.22 \(\times 10^{16}\) & 2.594436 & 0.245601 & 0.005959 & 0.015628 & 0.025564 \\ Linear & 0.551602 & 0.370364 & 0.107414 & 0.411879 & 1.416933 & 0.190599 & 0.003588 & 0.038805 & 0.066233 \\ XGB & 0.96898 & 0.905209 & 0.016171 & 0.911202 & 0.705034 & 0.074994 & 0.000402 & 0.013361 & 0.050822 \\ Cat Boost & 0.858901 & 0.807068 & 0.032914 & 0.811086 & 0.669619 & 0.119967 & 0.001098 & 0.023656 & 0.100901 \\ Krnl Rdg & \(-\)7.52032 & \(-\)13.6857 & 2.505323 & -11.55584 & 4.595392 & 0.005294 & 0.192135 & 0.176634 & 0.232028 \\ Rnd Frst & 0.96632 & 0.958924 & 0.0700707 & 0.961693 & 0.2120709 & 0.0066961 & 0.00106 & 0.0011924 & 0.052773 \\ SVR & 0.942443 & 0.906048 & 0.016028 & 0.917416 & 0.277325 & 0.10573 & 0.000419 & 0.019669 & 0.109814 \\ GB & 0.96921 & 0.90652 & 0.015947 & 0.9123 & 0.699241 & 0.073867 & 0.000396 & 0.013157 & 0.050819 \\ MLP & 0.960045 & 0.950374 & 0.08466 & 0.955407 & 0.19942 & 0.07822 & 0.00195 & 0.01403 & 0.081658 \\ EI Net & 0.552002 & 0.372548 & 0.107041 & 0.414041 & 1.421286 & 0.190074 & 0.00358 & 0.038717 & 0.067043 \\ SGD & 0.543944 & 0.339704 & 0.112644 & 0.413088 & 1.391616 & 0.198231 & 0.003718 & 0.040097 & 0.09443 \\ LGBM & 0.431142 & 0.206002 & 0.135453 & 0.250426 & 1.674311 & 0.187298 & 0.004571 & 0.038722 & 0.097639 \\ Lasso & 0.551684 & 0.370802 & 0.107339 & 0.412313 & 1.417802 & 0.109494 & 0.003586 & 0.038787 & 0.066395 \\ LARS & \(-\)0.00198 & \(-\)0.03552 & 0.176656 & \(-\)2.22 \(\times 10^{16}\) & 2.594436 & 0.245601 & 0.00595 & 0.051628 & 0.205564 \\ RANSAC & 0.50913 & 0.492829 & 0.086522 & 0.49596 & 1.88031 & 0.136337 & 0.003298 & 0.02977 & 0.0827 \\ Theil-Sen & \(-\)5.46248 & \(-\)10.282 & 1.924665 & \(-\)8.75078 & 0.412532 & 0.0805855 & 0.11491 & 0.156262 & 0.226623 \\ Huber & 0.46951 & 0.478527 & 0.088961 & 0.479205 & 1.9979051 & 0.132971 & 0.034422 & 0.02956 & 0.090452 \\ Quantile & 0.183056 & 0.23943 & 0.129751 & 0.316501 & 2.459665 & 0.144412 & 0.004771 & 0.033602 & 0.058078 \\ Pass Agr & \(-\)16.7823 & \(-\)26.9486 & 4.767939 & \(-\)18.8068 & 3.865447 & 2.092106 & N/A & 0.380279 & 2.171411 \\ Tweedie & 0.551603 & 0.37037 & 0.107413 & 0.411885 & 1.416945 & 0.190597 & 0.035888 & 0.038805 & 0.066235 \\ GPR & 0.49625 & 0.181773 & 0.139587 & 0.260354 & 1.158412 & 0.222099 & 0.004410 & 0.042428 & 0.075856 \\ KNeigh & 0.96924 & 0.949614 & 0.008596 & 0.94964 & 0.355 & 0.063333 & 0.000202 & 0.0111335 & 0.035 \\ Dummy & \(-\)0.00198 & \(-\)0.03552 & 0.176656 & \(-\)2.22 \(\times\) 10^{16}\) & 2.594436 & 0.245601 & 0.00595 & 0.051628 & 0.205564 \\ Poisson & \(-\)0.0017 & \(-\)0.03552 & 0.176656 & \(-\)2.22 \(\times\) 10^{16}\) & 2.594436 & 0.245601 & 0.005959 & 0.051628 & 0.205564 \\ AdaBoost & 0.960807 & 0.981735 & 0.01847 & 0.909818 & 0.71 & 0.093959 & 0.00453 & 0.016723 & 0.085143 \\ Dec Tree & 0.968853 & 0.904517 & 0.016289 & 0.910219 & 0.71 & 0.074304 & 0.000405 & 0.013235 & 0.050795 \\ \hline \hline \end{tabular}  
* R\({}^{2}\) or coefficient of determination analyzes how differences in one variable can be explained by a difference in a second variable. Mean squared error (MSE) or mean squared deviation (MSD) calculates the amount of error in statistical models with the average squared difference between the observed and predicted values. When the MSE equals zero, the model has no error. The square root of the MSE calculates the root mean squared error (RMSE), giving the natural data units. MSE is analogous to the variance, and RMSE is analogous to the standard deviation. MAE (mean squared error) is the amount of error in your measurements and represents the difference between the measured value and “true” value, a risk metric corresponding to the expected value of the absolute error loss or -norm loss. FVS (explained variance score) or explained variation is used to measure the discrepancy between a model and actual data. Max Err. (maximum error) calculates the maximum residual error, a metric that captures the worst-case error between the predicted value and the true value. MSL (mean squared log error) calculates a risk metric corresponding to the expected value of the squared logarithmic (quadratic) error or loss. MAP (mean absolute percentage error) or mean absolute percentage deviation (MAPD) is an evaluation metric for regression problems, sensitive to relative errors.  
\end{table}