## 3 Results
### Machine Learning Analysis
To synthesize these findings, Table 5 presents a side-by-side ranking of the top features by normalized importance score across models. Although the absolute values differ slightly depending on the method, the relative influence of the variables remains largely consistent. The comparative feature importance table highlights both convergence and variability across models. All four algorithms consistently identified _NumericImpactRespons_ as the most predictive feature, capturing participants' perceived impact of smoking on family relationships. Additional variables such as _NumericConflictRespons_ and _FamilySmoking_ showed moderate influence in specific models (notably SVM and k-NN), while demographic factors such as age and gender had minimal impact. These results suggest that psychosocial and relational variables play a more central role in model predictions than basic demographic characteristics, reinforcing the importance of family dynamics in smoking behavior analysis. The features that were most influential in supervised models also contributed significantly to separation in the PCA projection. _NumericImpactRespons_ and _ConflictRespons_, which ranked highest across multiple algorithms, were also key drivers of visible clustering in the reduced two-dimensional feature space. This consistency between supervised and unsupervised analyses supports the robustness and interpretability of the selected predictors. Confusion matrices (Figure 10) highlighted the strengths and weaknesses of these models. The ensemble model consistently minimized misclassification across all response categories, while the decision tree and k-NN models exhibited moderate misclassification rates, capturing general trends but struggling with ambiguous cases. The SVM model demonstrated significant classification errors, underscoring its limitations in capturing nuanced distinctions among complex response variables. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** \\ \hline Decision Tree * & 83.33 & 0.79 & 0.70 & 0.74 \\ Ensemble Method * & 93.33 & 0.91 & 0.91 & 0.91 \\ SVM * & 80.00 & 0.60 & 0.75 & 0.67 \\ k-NN * & 90.00 & 0.90 & 0.82 & 0.86 \\ \hline \hline \end{tabular}
* Top feature: NumericImpactRespons for all models.  
\end{table} 
Table 4: Performance evaluation of the models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **Decision Tree** & **Ensemble** & **SVM** & **k-NN** \\ \hline Gender & 0.0000 & 0.0085 & 0.0000 & 0.0000 \\ Age & 0.0000 & 0.0000 & 0.0167 & 0.0000 \\ Do you smoke & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ CleanedSmokingFrequency & 0.0000 & 0.0023 & 0.0000 & 0.0000 \\ When did you start smoking & 0.0000 & 0.0031 & 0.0067 & 0.0000 \\ NumericFamilySmoking & 0.0000 & 0.0000 & 0.0133 & 0.0000 \\ NumericConflictResponses & 0.0381 & 0.0000 & 0.0000 & 0.0080 \\ NumericImpactResponses & 0.1875 & 0.1459 & 0.1867 & 0.2433 \\ NumericAcceptanceResponses & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative feature importance across machine learning models (normalized scores).