## 6 Analysis of Experiment Results
### NLP Transformation Accuracy
While the dependency between number of steps and result quality does not appear to be linear, it shows that the best results are also produced by the amount of steps around 50-100, while reducing in accuracy at larger parameters. The amount of steps also seems to greatly increase computational time. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Number of Similar Samples** & **Predicted Values** & **Actual Values** \\ \hline
50 & 370.20, 2.54, 0.48 & 365.25, 2.54, 0.45 \\ \hline
100 & 370.82, 2.53, 0.46 & 365.25, 2.54, 0.45 \\ \hline
250 & 371.17, 2.54, 0.47 & 365.25, 2.54, 0.45 \\ \hline
500 & 372.60, 2.54, 0.44 & 365.25, 2.54, 0.45 \\ \hline
1000 & 367.74, 2.55, 0.47 & 365.25, 2.54, 0.45 \\ \hline
2500 & 370.81, 2.58, 0.47 & 365.25, 2.54, 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for experimental unit conversion approach with 2 known traits and 100 experiments depending on the number of similar samples. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Similar Samples** & **Before Transformation** & **After Transformation** \\ \hline
50 & [23,143.48, 1.0, 163.92, 73.96, 135.0, & [22,431.0, 1.0, 163.0, 72.0, 135.0, 80.0, \\  & 80.0, 1.0, 2.0, 0.0, 0.0, 0.0] & 1.0, 2.0, 0.0, 0.0, 0.0] \\ \hline
100 & [23,058.68, 1.0, 158.69, 128.300, 140.0, & [22,601.0, 1.0, 158.0, 126.0, 140.0, 90.0, \\  & 90.0, 2.0, 2.0, 0.0, 0.0, 1.0] & 2.0, 2.0, 0.0, 0.0, 1.0] \\ \hline
250 & [20,540.0, 1.0, 170.0, 72.0, 120.0, 80.0, & [20740.02, 1.0, 169.60, 72.238, 120.0, \\  & 2.0, 1.0, 0.0, 0.0, 1.0] & 80.0, 2.0, 1.0, 0.0, 0.0, 1.0] \\ \hline
500 & [19,401.56, 2.0, 182.63, 106.37, 180.0, & [19,066.0, 2.0, 183.0, 105.0, 180.0, 90.0, \\  & 90.0, 3.0, 1.0, 0.0, 1.0, 0.0] & 3.0, 1.0, 0.0, 1.0, 0.0] \\ \hline
1000 & [21,300.94, 1.0, 170.77, 71.54, 120.0, & [20,540.0, 1.0, 170.0, 72.0, 120.0, 80.0, \\  & 80.0, 2.0, 1.0, 0.0, 0.0, 1.0] & 2.0, 1.0, 0.0, 0.0, 1.0] \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Individual sample transformation, the experimental unit conversion approach. As expected, the number of steps makes the computation time grow, but a larger number of steps does not necessarily equal higher accuracy. Here, we see that the best coefficient for years achieved in only 10 steps, whereas the better ones for inches and pounds are achieved in 250 and 50 steps, respectively. A possible future improvement to the method may be modifying it to use different amounts of steps for different features (Table 12). Depending on the parameters, the converted values suffer various degrees of distortion, sometimes missing their mark by nearly two years. The distortion becomes even more evident on the dataset merging test that did not include label interpretation (which was switched off due to it automatically converting the example trait), where only two traits used to for the method are achieved by converting the other sets' numericals into booleans, therefore adding a degree of inaccuracy, as shown in Table 14.  
With the best result being a coefficient of 471.80, the worst one being that of 542.29, and both being far from the intended coefficient of 365.25, it is clear that the approach is a lot less reliable when preceded by multiple other transformations to the data, which have already added a degree of distortion.