## 4 Experimental Settings up
### Production Planning Process with M5P Algorithm  
The pruned tree shows that the decision-tree algorithm was applied and that the nodes were organized in a linear model. A linear model is generated as many times as there are nodes in the decision tree and generates multiple formulas. In the above formula, the final value is the vertical set of data from the preprocessed input data in Figure 11.  
Figure 10: M5 unpruned model tree generated by the M5P algorithm using Weka. Figure 11: Linear model created with the M5P algorithm using Weka. Through the GUI of the Weka Library, you can check the graphs and metrics of the M5P algorithm results for each equipment and model. Objects with the prefix \"predicted\" have a Y-value, indicating that they are the result of that machine learning. An example of one of the resulting values can be seen in the Figure 12. A model performance chart is used to evaluate the prediction accuracy and performance of the model by visualizing the relationship between predicted and actual values. The predicted value is called \"predictedREAL TACT\" and the actual value is called \"REAL TACT\". The chart shows how well the predicted and actual values match. The difference between the predicted and actual values is visualized. In this study, the target data for prediction were Real tact time and the Y value is Predicted real tact time as a result of the algorithm. All metadata are composed of different colors and can be classified in the graph, the composition data of the result value can be checked. Various metrics have been used to evaluate the performance of machine-learning regression models, including the mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R-squared). MSE is a metric that squares the difference between the predicted value and the actual value, then calculates the average. The smaller the MSE, the smaller the model's prediction error. MAE is a metric that calculates the average of the absolute differences between predicted and actual values. The smaller the MAE, the smaller the model's prediction error. R-squared is a measure of how well a model explains the given data. It represents the percentage of the total variability in the given data that can be explained by the model. The coefficient of determination has a value from 0 to 1, with values closer to 1 indicating that the model explains the data well. These metrics are used to evaluate and compare the performance of the regression model in various aspects. In Figure 13, the evaluation metrics for the performance of the regression model can be seen. Shows the results of machine learning performance metrics with the optimal values of hyperparameter settings -N, -U, and -M 6.0 applied to the test. Figure 12: M5P algorithm Results predicted by Machine learning. === Evaluation result ===  
Scheme: M5P  
Options: -N -U -M 6.0  
Relation: R-data-frame-weka.filters.unsupervised.attribute.Remove-R1  
Correlation coefficient 0.9598  
Mean absolute error 0.2791  
Root mean squared error 1.26  
Relative absolute error 8.7515 $  
Root relative squared error 28.0905 $  
Like tact time, yield can also be predicted using the M5P algorithm, and the resulting value, M5P Linear Regression, is reflected as new master data. Input the results of machine learning predictions into a  
Figure 14: M5P Algorithm in Learning reduces learning Tact time Compared to Original Data. Figure 15: Compare the Quantities of the Existing Plan and The Machine Learning predictive Model Plan. Increasing the input quantities and working hours are important issues in production management. If these fluctuations can be predicted and considered in an actual production plan, the accuracy of the schedule, efficiency of the production line, and management of resources can be better controlled. This would, in turn, enable production process optimization at an unprecedented level.