# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 3 Experiment and Discussion
### Feature Screening  
Among the methods of feature selection, the correlation coefficient method and variance selection method are the traditional feature selection methods, but they cannot deal with high-dimensional features and the complex relationships between features. However, tree-model-based feature selection methods can automatically capture the nonlinear relationships between features, handle missing values, and evaluate features. In the tree model, shallow nodes tend to have a better feature classification ability, meaning that important features are more likely to appear in shallow nodes and appear more frequently, so we can use the index of the number of times each feature appears in the tree model to calculate its importance ranking. This applies to high-dimensional data problems and complex data distributions. Therefore, in this study, the variance selection method, the correlation coefficient method, and the tree-model-based feature selection method are used for feature selection, respectively, and an external evaluator, XGBoost, is used for the evaluation of feature subsets to compare the performance advantages and disadvantages of these three feature selection methods. In the screening process of feature subsets, parameters such as a learning rate of 0.3, single tree depth of 6, and random sampling ratio of 0.7 are used to select \(R^{2}\) as the evaluation index. After comparing the feature selection schemes, the tree-model-based feature selection method is determined to be more effective, as shown in Table 1.  
Since we collected the features of air conditioning products before, in this section, we rank the importance of the features of the tree model, excluding the product name and product number. The importance ranking of the influencing factors is shown in Table 2, and the influencing factors with the top 30 importance rankings are used in this study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Selection Method** & **Goodness-of-Fit (\(R^{2}\))** \\ \hline Feature complete & 0.7016 \\ Variance selection method & 0.7512 \\ Correlation coefficient method & 0.7608 \\ Tree-model-based feature selection method & 0.7996 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of feature screening goodness-of-fit. The significance analysis demonstrates that different factors have varying degrees of influence on demand prediction. The top two shipment data and total sales data are the primary link between a company's supply chain and the market, and they are critical for businesses to effectively estimate market demand. Sales volume serves as a crucial indicator for gauging product quality, as it is reflective of consumer preference. Consequently, a high sales volume may suggest that the product is capable of aligning with consumer demand in terms of both quality and price. The proper utilization of these data can provide better feedback on consumer demand while also increasing product sales and competitiveness. Furthermore, the third component is e-commerce festival data, which include some of the most prominent e-commerce festivals (for example, 618 and Double 11). This aspect demonstrates that e-commerce festivals significantly impact current customer behavior regarding home appliances. The fourth component is the seasonal factor, which indicates that air conditioner sales are affected by the seasons, and when the temperature rises in the summer, the sales volume generally rises. When temperatures drop in the autumn and winter, sales often fall. The fifth and sixth criteria are production and inventory, respectively. High output and inventory means can swiftly meet market demand; demand forecasting also has some reference relevance. The seventh factor is the product's pricing. Price is an essential factor in determining whether or not a consumer will buy a product, especially when goods' qualities are identical. Buyers are more likely to choose the lowest-priced option. There is a consensus that price can have a positive or negative effect on sales.