## 4 Artificial Intelligence Model Evaluation
### Results  
In this paper, in order to evaluate the performance of artificial intelligence models used for estimating the remaining capacity of lithium-ion batteries, mean squared error, mean absolute error, R-squared score, and the difference between the predicted value and the actual value of the model was measured using root mean squared error, etc., and the predictive accuracy of the model was evaluated as shown in Table 2.  
Analysis of evaluation metrics for each model in Table 2 shows that the mean squared error (MSE) of the linear regression model is a very low 0.000000, which means that there is almost no error between predicted and actual values. Also, the mean absolute error (MAE) is very low at 0.000000, which indicates that the error between predicted and actual values is very small. Also, the R-squared score (coefficient of determination) is very high at 1.000000, which means that the model can explain the variability of the dependent variable very well. Therefore, it can be said that there is almost no error between the predicted value and the actual value because the R-squared value is high. MSE: 0.000000, MAE: 0.000409, R-squared score: 0.999988, RMSE: 0.000844 for the decision tree model. The decision tree model shows very good results in MSE, MAE and R-squared score. However, because the RMSE values are relatively high, there can be large errors between the model's predicted values and the actual values, which means that the decision tree model can produce large prediction errors for some samples. The results of the random forest model show MSE: 0.000002, MAE: 0.000303, R-squared score: 0.999963, RMSE: 0.001463. The random forest model shows very good results for MSE, MAE and R-squared score. However, the RMSE value is also relatively low, but with a smaller error than the decision tree model. Random forest models improve predictive performance by combining multiple decision trees via an ensemble method, which allows the model to consider different characteristics and learn different patterns in the data, giving relatively more accurate predictions. The results of the neural network model are MSE: 13.786497, MAE: 2.333469, R-squared score: \(-\)234.931174, RMSE: 3.713017. The neural network model has relatively high MSE, MAE, and RMSE values and a negative R-squared score, indicating that the model's predictive performance is very poor and the error from the actual value is large. Therefore, neural network models have poor predictive performance compared to other models and may not be suitable for estimating the remaining capacity of lithium-ion batteries. The results of the ensemble (linear regression + decision tree + random forest + neural network) model show MSE: 0.861601, MAE: 0.583346, R-squared score: \(-\)13.744752, RMSE: 0.928225. The ensemble model used here is a model that combines linear regression, decision tree, random forest, and neural network models, and the evaluation metrics show lower performance than other models. The MSE, MAE, and RMSE values are large, and the R-squared score values are negative, suggesting that the ensemble model may have lower predictive performance than the other models or that the models may be inconsistent. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **R-Squared Score** & **RMSE** \\ \hline Linear regression & 0.000000 & 0.000000 & 1.000000 & 0.000000 \\ Decision tree & 0.000000 & 0.000409 & 0.999988 & 0.000844 \\ Random forest & 0.000002 & 0.000303 & 0.999963 & 0.001463 \\ Neural network & 13.786497 & 2.333469 & \(-\)234.931174 & 3.713017 \\ Ensemble (linear regression + decision tree + random forest + neural network) & 0.861601 & 0.583346 & \(-\)13.744752 & 0.92822 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The result values of evaluating the performance of artificial intelligence models.