The evaluation criterion is the root mean squared error (RMSE) between predicted ratings and true ones.
评判标准是预测打分和真实值之间的均方误差(RMSE)。
Examples in Test1 are used for calculating the scores shown on the Leaderboard: RMSE for Track1 and Error rate for Track2.
test1里的例子是用来计算领先选手排行榜(Leaderboard)上的分数：track1的RMSE和Track2的错误率(Errorrate)。
The process of which is to solve the integer programming problem with the objective function that RMSE is minimal.
选择最优参数的过程即是对以均方根误差最小为目标的整数规划问题的求解过程。
The results show that the root mean square error(RMSE) of the control points obtained by the workflow is lower than 0.5 pixels.
结果表明，使用该方法选取的控制点均方根误差（RMSE）可以控制在0.5个像素以内。
The results of the test data indicate that the prediction system is reliable and the root of mean square error (RMSE) is about 15%.
对测试数据的预测结果表明，该预测系统能够可靠工作，预测结果的均方根 误差在 15%左右。
Testing of the monitoring models with independent dataset indicated that the predictive precision (R2 ) was 0.6824, and RMSE was 0.4052.
经不同年际独立试验数据的检验，叶片碳氮比监测模型的预测精确R2为0.6824，根均方差（RMSE）为0.4052。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
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