## 3 Model Building and Algorithm Design
### Prediction Algorithm Design
 } \\ \hline \multirow{4}{*}{Times} & Working face 1 & 12 & 38 & 12 & 0 & 36 & 2 \\  & Working face 2 & 8 & 42 & 7 & 1 & 41 & 1 \\ \cline{1-1}  & Working face 3 & 11 & 39 & 10 & 1 & 38 & 1 \\ \cline{1-1}  & Working face 4 & 11 & 39 & 10 & 1 &



evaluations are relatively high, which preliminarily confirms the feasibility of logistic regression for the mine pressure prediction model. ### Experimental Analysis

#### 4.4.1 Data Credibility Analysis

[PERSON]'s alpha was used to analyze the credibility of the sample data before and after preprocessing, and the calculation formula is shown in Formula (24). \[\alpha=\frac{n}{n-1}\left(1-\frac{\sum S_{1}^{2}}{S^{2}}\right) \tag{24}\]

In the formula, n represents the number of selected evaluation indicators affecting the mine pressure hazard, \(S_{1}^{2}\) represents the variance in the i-th evaluation indicator in all samples, and \(S^{2}\) represents the variance in the sum of all evaluation indicators in all samples. Considering the influence of the data obtained through different preprocessing methods on the prediction model, eight different data preprocessing methods were designed, and the [PERSON]'s alphas of the processed data were calculated, respectively, to compare and analyze the credibility of the data. The comparison of the pretreatment methods is shown in Table 4.

The [PERSON]'s alphas of the sample data obtained with the eight different preprocessing methods were calculated using Formula (24), and the results are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Outlier Processing** & **Missing Value Processing** & **Standardized Processing** \\ \hline
1 & Non-processed & Fill the median & Min–max standardization \\
2 & Non-processed & Fill the median & Z-score standardization \\
3 & Non-processed & Delete missing values & Min–max standardization \\
4 & Non-processed & Delete missing values & Z-score standardization \\
5 & 3\(\sigma\) principle to remove outliers & Fill the median & Min–max standardization \\
6 & 3\(\sigma\) principle to remove outliers & Fill the median & Z-score standardization \\
7 & 3\(\sigma\) principle to remove outliers & Delete missing values & Min–max standardization \\
8 & 3\(\sigma\) principle to remove outliers & Delete missing values & Z-score standardization \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different methods of data preprocessing. Figure 4: [PERSON]’s alphas of the different data preprocessing methods. It can be seen from Figure 5 that the outlier processing of the first four groups of data adopts the method of non-processing, the Cronbach's alphas of the obtained data are all less than 0.6, and the credibility of the data is very poor. The Cronbach's alpha of the data obtained by deleting missing values is larger than that obtained by filling the median. The Cronbach's alpha of the data obtained by standardizing the data using Z-score standardization is larger than that of min-max standardization. In this paper, the 3\(\sigma\) principle, deleting missing values, and Z-score standardization are used to preprocess the data. The data obtained through this preprocessing method have the highest Cronbach's alpha, greater than 0.9. It can be seen that the sample data obtained using this method have the highest credibility. #### 4.4.2 Experimental Design

Experiment 1:

Experimental objective: Verify the influence of eight different data preprocessing methods on the prediction model. Experimental process: The data processed with these eight different data preprocessing methods were fitted and solved, respectively, for the logistic regression model of mining pressure hazard, and the accuracy, recall, and F1-scores of the predicted results were calculated, respectively. Experiment 2:

Experimental objective: Verify the influence of the selected number of rating indicators on the prediction model.