## 3 Results
### Machine Learning Analysis
Similarly, the k-NN model was tuned by changing the number of neighbors (k) and using cross-validation to find the best balance of underfitting and overfitting. The best performance was obtained when k was adjusted to 5, which adequately captured the underlying data patterns without becoming too complicated. In this context, several key features were used in the models:  
* _NumericImpactResponses_--questions assessing the impact on family relationships;
* _NumericConflictResponses_--questions about family conflicts;
* _NumericAcceptanceRespons_--questions about family acceptance of smoking;
* _NumericFamilySmoking_--question whether anyone in the family smokes;
* _CleanedSmokingFrequency_--represents the individual's smoking frequency. In this study, a decision tree model was developed to classify data based on two primary features: _NumericImpactResponses_ and _NumericConflictResponses_. Although the algorithm initially had access to nine features, it automatically selected these two as the most relevant for predicting outcomes. The structure of the decision tree is hierarchical (Figure 5), with the root node splitting the dataset based on the value of _NumericImpactResponses_, applying a threshold of 0.495. Instances with _NumericImpactResponses_ values below this threshold are directed to the left child node, while those with values equal to or exceeding 0.495 are directed to the right child node. This primary division reflects the perceived impact of smoking on relationships, as measured by _NumericImpactResponses_, and serves as the foundation for subsequent classifications. Figure 4: Self-reported smoking frequency among participants. At the second level of the tree, the child nodes resulting from the initial split are further divided based on the value of _NumericConflictRespones_, with a threshold of 0.625. The terminal nodes correspond to a unique combination of values for _NumericImpactResponses_ and _NumericConflictRespones_:  
When _NumericImpactRespons_ is less than 0.495, the model predicts an outcome of 0.33. When _NumericImpactRespons_ is greater than or equal to 0.495 and _NumericConflictRespons_ is equal to or above 0.625, the predicted outcome is 1. When _NumericImpactRespons_ is greater than or equal to 0.495 and _NumericConflictRespons_ is below 0.625, the predicted outcome is 0.66. Beyond the structural insights, the importance of the selected features was analyzed to understand their relative contribution to the model's performance. The results demonstrate that for the decision tree (Figure 6), _NumericImpactRespons_ is far more influential, with an importance score of 0.1875, compared to _NumericConflictRespons_, which has a score of 0.03807. This finding suggests that the perceived impact of smoking on relationships (_NumericImpactRespons_) is the dominant driver of decision-making within the model, while conflict-related considerations (_NumericConflictRespons_) play a secondary role. The feature importance analysis is particularly significant because it underscores the model's capacity to discern and prioritize the most predictive features from a broader set of  
Figure 5: Decision tree for predicting outcomes based on _NumericImpactRespons_ and _NumericConflictResponses_. Figure 6: The feature importance analysis by decision tree. variables. The substantial difference in importance scores indicates that _NumericImpactResponses_ is central to understanding the relationships in the data, aligning with its role as the root node's splitting criterion. The ensemble method aggregates feature importance (Figure 7 across multiple base learners (e.g., decision trees) to provide a robust measure of feature contribution. In this case, the most influential feature is _NumericImpactResponses_, with a score of 0.1459, far exceeding the contributions of other features. Other features, such as _Gender_ (0.0085), _CleanedSmokingFrequency_ (0.0023), and _When did you start smoking_ (0.0031), contribute marginally, while several features exhibit no measurable importance.