标题： 使用机器学习模型从线强度预测等离子体温度 显示英文标题

标题： Predicting Plasma Temperature From Line Intensities Using ML Models

摘要： 在这项工作中，使用ML模型预测了通过实施Na-like氪的CR模型获得的数据集中的等离子体温度。研究中包括的模型有：线性回归、套索回归、支持向量回归、决策树、随机森林、XGBoost、多层感知器和卷积神经网络。为了评估这些模型，我们使用了平均绝对误差、均方误差和R²评分作为指标。在我们的研究中，与其他考虑的模型相比，随机森林表现最好。研究得出结论，线强度与等离子体温度之间的复杂关系可以通过ML模型捕捉，并且它们可以用于高精度地预测温度。 显示英文摘要

摘要： In this work, ML models were used to predict the plasma temperature using the dataset obtained by implementing the CR-model for Na-like Krypton. The models included in the study are: Linear Regression, Lasso Regression, Support Vector Regression, Decision Trees, Random Forest, XGBoost, Multi-layer Perceptron and Convolutional Neural Network. For evaluating the models we used Mean Absolute Error, Mean Squared Error and R^2 Score as metrics, In our study Random Forest performed best as compared to other model considered, the study conclude that complex relation between the line-intensities and Plasma temperature can be capture by ML models and they can be used to predict the temperature with high accuracy.