# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 4 Experimental Evaluations
### Results & Discussion
The evaluation of the feature window size involves assessing various parameters, and the outcomes indicate that altering the window size has a substantial impact on the results. A smaller window size accommodates numerous features, which extends training time but yields inaccurate results. Figure 18 shows the results without the feature selection technique, and the plot shows results for windows with 100, 250, 500, 1000, and 2000 frames, respectively. This visualization demonstrates how the model's accuracy is affected by varying window sizes. Smaller window sizes exhibit higher root mean squared errors, while increasing the window size results in a reduction of the overall error. Notably, the window size of 500 frames consistently produces the most reliable results. All algorithms perform well with this window size, eliminating the need for feature selection. Figure 18: Leave-one-out results without recursive feature engineering. Figure 19 shows the results with recursive feature selection with Cross-Validation (RFECV) technique in which feature selection is made for each fold separately. The Linear model has shown poor results with the feature selection technique. Apart from the linear models, the windows with sizes greater than 500 frames have performed better for the given parameters. By excluding the linear models, we can say that the window with aggregation performed per 2000 frames has performed the best. Feature selection serves to reduce the algorithm's complexity and training time by eliminating non-relevant features based on their scores. However, it's important to note that sometimes it can have an adverse impact on the overall accuracy of the models. Our findings reveal that none of the linear models, namely the Gamma Regressor, Bayesian Ridge Regressor, and Theil-Sen Regressor, performed well when feature selection was applied, as evidenced in Figure 19. However, the linear models have given a good root mean squared score without the feature selection technique as can be noted from Figure 18. A linear model, the Theil-Sen regressor, gives the best RMSE score for the model training without the feature selection. The results show that feature selection consistently performed better for non-linear models. However, interestingly, the linear models produced the best results without feature selection, reflecting the importance of the complete set of features used for making predictions with the linear models. This observation highlights different behaviors of linear and non-linear models when it comes to feature selection. While non-linear models may benefit from a reduced feature set, linear models demonstrate superior performance when all features are considered. Meanwhile, the boosting methods such as XGBoost, and Gradient Boosting regressor methods have shown mixed results for different parameters such as the size of the window or the aggregation performed on that window. Feature selection does reduce the overall processing speed by excluding unnecessary features, but in our scenario, it comes at the cost of better model accuracy. The non-linear methods are more flexible and can capture complex relationships between features and the target variable. Feature selection enables the model to focus on the most discriminative features for capturing these non-linear decision boundaries. However, the linear models inherently rely on the linear relationships between features and the target variable, making them less sensitive to irrelevant features. Due to these reasons, we can see from the analysis that linear models are performing slightly better without the feature selection technique compared to the non-linear models. Choosing the suitable aggregation to perform on the window is also crucial as it impacts the model's performance and the total number of features used to train the model, impacting its training speed. As discussed, we have performed three aggregation types on each window separately: sum, mean, and a combination of both sum and mean.