翻译成英文 在问题3中，我们首先利用秩和比综合评价法(RSR)对难度进行定义，对（1，2，3，4，5，6，X）的相关百分比进行综合评价，各个单词单词难度进行综合评价得分。划分为难易中三档，之后分别利用逻辑回归和CNN训练分类模型来对单词进行难度分类，同时与决策树，随机森林，LGBM，XGBOOST对单词进行难度分类预测的结果进行了对比研究，防止了模型的过拟合并利用模型对eerie的难度进行了分类

In Question 3, we first used Rank and Score Comprehensive Evaluation (RSR) to define the difficulty and give a comprehensive score to the related percentages (1, 2, 3, 4, 5, 6, X) of each word. It was divided into three levels of difficulty, then we used Logistic Regression and CNN to train the classification model to classify the difficulty of the words, and compared the results with Decision Tree, Random Forest, LGBM, and XGBOOST to predict the difficulty of the words, preventing the model from overfitting and using the model to classify the difficulty of 'eerie'.

相关问题

翻成英语 在问题3中，我们首先利用秩和比综合评价法(RSR)对难度进行定义，对（1，2，3，4，5，6，X）的相关百分比进行综合评价，各个单词单词难度进行综合评价得分。划分为难易中三档，之后分别利用逻辑回归和CNN训练分类模型来对单词进行难度分类，同时与决策树，随机森林，LGBM，XGBOOST对单词进行难度分类预测的结果进行了对比研究，防止了模型的过拟合并利用模型对eerie的难度进行了分类

答：我们首先使用秩和比综合评价法（RSR）定义了单词的难度，并对（1，2，3，4，5，6，X）的相关百分比进行综合评分，划分为难易中三档。然后，我们使用逻辑回归和CNN训练分类模型，与决策树，随机森林，LGBM，XGBOOST进行对比研究，以防止模型过拟合，并利用模型对单词的难度进行分类预测。

翻译成英文 对于问题三，我们首先利用秩和比综合评价法(RSR)对难度进行定义，分为难、中易三档，逻辑回归和CNN训练分类模型来对单词进行难度分类，其中逻辑回归的训练集准确度为0.778，测试集准确度为0.750。CNN训练的训练集和测试集上训练网络训练集F1:0.822 测试集F1:0.76并对eeire进行了判断难度为中等

For question three, we first use Rank and Ratio Comprehensive Evaluation (RSR) to define the difficulty, divided into three grades of hard, medium and easy. Logistic regression and CNN are used to classify the difficulty of words, which the accuracy of the training set of logistic regression is 0.778, and the accuracy of the test set is 0.750. The F1 of the training set and test set of the CNN trained network is 0.822 and 0.76 respectively, and the difficulty of eeire is judged as medium.