# Data on the Construction Processes of Regression Models

This CSV dataset (numbered 1–8) demonstrates the construction processes of the regression models using machine learning methods, which are used to plot** Fig. 2–7**. The CSV file of 1.LSM_R^2 (plotting **Fig. 2**) shows the data of the relationship between estimated values and actual values when the least-squares method was used for a model construction. In the CSV file 2.PCR_R^2 (plotting **Fig. 3**), the number of the principal components was varied from 1 to 5 during the construction of a model using the principal component regression. The data in the CSV file 3.SVR_R^2 (plotting **Fig. 4**) is the result of the construction using the support vector regression. The hyperparameters were decided by the comprehensive combination from the listed candidates by exploring hyperparameters with maximum *R*^{2} values. When a deep neural network was applied to the construction of a regression model, *N*_{Neur.}, *N*_{H.L.} and *N*_{L.T.} were varied. The CSV file 4.DNN_HL (plotting **Fig. 5a)**) shows the changes in the relationship between estimated values and actual values at each *N*_{H.L.}. Similarly, changes in the relationships between estimated values and actual values in the case *N*_{Neur.} or *N*_{L.T. }were varied in the CSV files 5.DNN_ Neur (plotting **Fig. 5b)**) and 6.DNN_LT (plotting **Fig. 5c)**). The data in the CSV file 7.DNN_R^2 (plotting **Fig. 6**) is the result using optimal *N*_{Neur.}, *N*_{H.L.} and *N*_{L.T.}. In the CSV file 8.R^2 (plotting **Fig. 7**), the validity of each machine learning method was compared by showing the optimal results for each method.

__Experimental conditions__

Supply volume of the raw material: 25–125 mL

Addition rate of TiO_{2}: 5.0–15.0 wt%

Operation time: 1–15 min

Rotation speed: 2,200–5,700 min-1

Temperature: 295–319 K

__Nomenclature__

*N*_{Neur.}: the number of neurons

*N*_{H.L.}: the number of hidden layers

*N*_{L.T.}: the number of learning times