## 5 Experiment Design-Accuracy Verification of Process Evaluation Method
### Experimental Design  
This experiment is based on our proposed process evaluation algorithm as shown in Figure 2, using real experimental data to train a variety of machine learning process evaluation predictors, 80% of the data is used for training the model, 20% is used for prediction verification, and then through the three MAPE, RMSE, and R Squared indicators for comparative analysis. The proposed model is verified by numerical value [57]. Experimental environment: The desktop computer builds the experimental environment. The hardware equipment consists of an AMD Ryzen 3600x processor which is manufactured by Superway Semiconductor Corporation in Santa Clara, CA, USA, and sourced from Suzhou Industrial Park in Jiangsu Province, 32G memory, and 1T hard disk. In terms of software, we use the Windows 10 operating system, its version number is 21H2 and the Python 3.8 programming language, and conduct experiments on the Jupyter lab 3.0.1 programming tool. Data Sources  
By putting the Lishi Evaluation Auto Repair System into Hangzhou X Secondary Vocational School, a training experiment was carried out. A total of 69 students in two classes used the system to learn and collected their training process data. Use the techniques in Section 4 for data cleaning and use this data as the dataset required for the experiment. The experiment uses linear regression, decision tree regression, support vector machine regression (SVM regression), KNN regression, random forest regression five machine learning algorithms for testing. The performance of various machine learning evaluators is evaluated through experiments, mainly through three indicators: Mean Absolute Percentage Error (MAPE, Mean Absolute Percentage Error), Root Mean Squared Error (RMSE, Root Mean Squared Error), and R Squared (r2 score). The final experiments show that KNN regression has the most stable performance, and it is used to train the evaluator model and deploy it into the system. Algorithm 1 shows the pseudo-code of the prediction algorithm of this system. ```
1:\(\text{V}\{v_{1},v_{2},\ldots,\ v_{n}\}\), THRESHOLD, EVADATA
2:\(\text{EVADATA}\) // First, Eliminate outliers and fill in missing values 1: \(B\{b_{1},\ b_{2},\ldots,\ b_{N}\}\gets V[v_{1},v_{2},\ldots,\ \ldots,v_{n}]\) // Features with a training-set variance lower than the threshold should be removed 2:For each column features \(b_{i}\in B\)do
3:\(b_{i}



the R squared score value of KNN is the highest, indicating that the fitting degree of KNN algorithm model is higher and the prediction is more accurate. Figure 4 describes the RMSE values of the five algorithm prediction models, the value of RMSE ranges from 31.61% to 74.05%, the average AVG is 48.75%, it can be seen that the RMSE value of SVR is the largest, and the RMSE value of KNN is the smallest. The root mean square error is used to measure the deviation between the observed value and the true value, indicating that except for the prediction model of SVM and decision tree algorithm, the difference between the predicted value and the real result of other models is small, while the deviation between the predicted value and the real result of the prediction model of KNN algorithm is the smallest. Figure 5 describes the MAPE values of the five algorithm prediction models. MAPE itself is often used as a statistical indicator to measure the prediction accuracy. MAPE can describe the accuracy. The value range of MAPE of the prediction model of the five algorithms is 0.13-0.41%, and the average AVG is 0.24%, indicating that the prediction error of the evaluation model is very small. The KNN algorithm prediction model also has the smallest MAPE value, indicating that the KNN algorithm model has a smaller error in the prediction score and the original expert scoring. Figure 4: Root Mean Squared Error of five prediction models. Figure 3: R Squared index of five prediction models.