## 3 Evaluation Data and Related Algorithms
### XGBoost Algorithm and Application  
Machine learning, fundamentally, is similar to statistical regression analysis. Most machine learning algorithms are regression algorithms that seek the functional relationship between independent and dependent variables. Unlike other parametric tests, the biggest advantage of using machine learning algorithms in evaluations is that they do not make assumptions about the functional relationship between independent and dependent variables, thus avoiding human involvement and subjective biases.  
There are many machine learning algorithms. Commonly used ones include artificial neural networks, support vector machines, adaptive spline regression, and various Bagging and Boosting techniques. Among them, ensemble learning techniques, like Bagging and Boosting, have been a hot topic in machine learning research in recent years. They attempt to combine multiple learning models to reduce fitting errors and improve the prediction accuracy of individual models [33]. The main representative algorithm for Bagging is Random Forest, and for Boosting, there are algorithms like Adaboost, GBDT, and XGBoost. Currently, XGBoost allows the user to define the loss function, further increasing the model's generalization ability. In addition, a series of strategies, such as the greedy algorithm for finding the structure of added trees and the loss function and regularization term in the loss function, makes XGBoost more accurate in prediction compared to other methods, thus gaining wide application [34].  
Based on this, in this study, the national high-tech zones are classified into \"world-class parks\" and \"other parks\" categories. The XGB classifier from the XGBoost is used to train and fit the standardized indicator data from 2013 to 2017, and the accuracy rate, precision rate, recall rate, and F1 score [35] are used to evaluate the performance of the model; the grid search method is used for tuning hyperparameters to improve the models' performance in order to determine the feature indicators that have a significant influence on distinguishing excellent parks from other parks. The importance of each feature indicator is then ranked based on their weights. The frame of the structure is as follows (Figure 1):  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**National High-Tech**} & \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**R\&D**} & \multirow{2}{*}{**Internal**} & \multirow{2}{*}{**Park Added**} & \multirow{2}{*}{**Industrial**} & \multirow{2}{*}{**Operating**} \\
**Zones** & & & & & & & \\  & **Interprises** & **Full-Time** & **on R\&D Funds** & & & \\ \hline Zhongguancun & 1.000 & 1.000 & 1.000 & 1.000 & 0.959 & 1.000 \\ Shenzhen & 0.094 & 0.465 & 0.521 & 0.195 & 0.228 & 0.137 \\ Hangzhou & 0.091 & 0.290 & 0.315 & 0.147 & 0.258 & 0.103 \\ Zhangjiang & 0.242 & 0.498 & 0.615 & 0.518 & 1.000 & 0.363 \\ Wuhan & 0.138 & 0.455 & 0.401 & 0.251 & 0.418 & 0.226 \\ Suzhou industrial park & 0.115 & 0.320 & 0.234 & 0.140 & 0.358 & 0.093 \\ Chengdu & 0.086 & 0.201 & 0.154 & 0.147 & 0.331 & 0.111 \\ Hefei & 0.058 & 0.184 & 0.185 & 0.159 & 0.350 & 0.086 \\ Guangzhou & 0.173 & 0.450 & 0.341 & 0.159 & 0.310 & 0.134 \\ Xi’an & 0.180 & 0.315 & 0.363 & 0.296 & 0.632 & 0.211 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standardized value of evaluation indicators of national high-tech zones in 2017.