# Age Estimates from Name Characters
## 5 Discussion  
### Features' Impacts on Prediction Accuracy  
The model we proposed is based on six features: word meaning (W), fortune-telling feature (F), Chinese zodiac (Z), word radical (R), word pronunciation (P), and gender probability (G). These features can be categorized into two groups: direct features and contextual features. The Direct Features include word meaning and word pronunciation, which come directly from name characters. Taking the Chinese word \"\(\frac{\text{W}}{\text{W}}\)\" as an example, the word meaning is beauty; the word pronunciation is \"Mei\". The Contextual Features include fortune-telling feature, Chinese zodiac, word radical, and gender probability, which originate from the Taiwanese cultural, social, and historic background. According to our experience, the Chinese word \"\(\frac{\text{W}}{\text{W}}\)\" is highly likely to belong to a female name. The impact of these features on the prediction results can be discussed in three directions. First, considering how a single feature affects prediction results, the word meaning feature was found to be the most informative one. Using the word embedding feature alone, it can achieve 93.94% accuracy in gender prediction, as shown in Table 22, and an average error year of 3.9785 (equal to 0.80 age-interval) for age-interval prediction, as indicated in Table 23. However, the Chinese zodiac feature only provides 58.79% accuracy in gender prediction and generates an average error year of 8.8080 (equal to 1.76 age-interval) for age-interval prediction. The performance differences from different features are significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline Zodiac (Z) & 0.5879 & 0.5918 & 0.9416 & 0.7268 \\ Fortune-telling (F) & 0.5955 & 0.5969 & 0.9504 & 0.7333 \\ Radical (R) & 0.8111 & 0.8256 & 0.8625 & 0.8437 \\ Pronunciation (P) & 0.8766 & 0.8844 & 0.9056 & 0.8949 \\ Word embedding (W) & 0.9394 & 0.9411 & 0.9571 & 0.9490 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Single feature for gender prediction. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{3}{c}{**Average Year Error**} \\ \cline{2-3}  & **Single Name** & **Without Single Name** \\ \hline  & U & 11.1470 & 10.8600 \\  & S & 11.1837 & 10.0149 \\  & M & 16.3608 & 15.0794 \\ Unigram-based & P & 14.7213 & 13.8360 \\  & US & 8.3997 & 8.3001 \\  & UM & 8.8870 & 7.2471 \\  & UP & 9.9148 & 9.4274 \\  & W & 17.9851 & 18.3118 \\ Embedding-based & WP & 14.7012 & 15.2240 \\  & WUP & 11.5443 & 11.5808 \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Age-interval prediction on ethnic-Chinese Malaysian names. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline Zodiac (Z) & 0.5879 & 0.5918 & 0.9416 & 0.7268 \\ Fortune-telling (F) & 0.5955 & 0.5969 & 0.9504 & 0.7333 \\ Radical (R) & 0.8111 & 0.8256 & 0.8625 & 0.8437 \\ Pronunciation (P) & 0.8766 & 0.8844 & 0.9056 & 0.8949 \\ Word embedding (W) & 0.9394 & 0.9411 & 0.9571 & 0.9490 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Single feature for age-interval prediction. Second, if we utilize word-embedding with one-hot encoding as the basis, combining with only one other feature, the word pronunciation feature most successfully predicts gender, with 94.48% accuracy. This is very close to the highest level of 94.64% accuracy with multiple features. The combination of word embedding with other single features can achieve similar performance as well, as shown in Table 24. As for the age-interval prediction, the word-embedding feature combined with Chinese zodiac reaches the lowest average error year of 3.6481 (equal to 0.73 age-interval), as shown in Table 25. Therefore, word embedding with Chinese zodiac is the most effective feature pair for age estimation.