坝体位移随机森林极限梯度提升预测模型为了加强尾矿库的安全稳定管理,提高溃坝预测预警水平,以坝体位移为研究对象,安全监测数据为研究基础,提出一种基于特征递归消除与随机森林和极限梯度提升的尾矿坝坝体位移预测模型,并与XGBoost,LSTM神经网络,BP神经网络,SVR等预测模型对比,以验证其预测效果.结果表明:所提出模型平均相对...
2. 特性筛选的方法3. RFE特征筛选法是怎么做的4. 相关系数怎么求5. 怎么平衡样本6. Lightgbm和xgboost的区别7. Stacking是怎么模型融合的8. 反问二轮技术面,做的业务比较多,慢慢地在使用大模型平安寿险 算法工程师 二面 11.08 35min自我介绍+项目,说的很详细 花了25min2. 介绍BN原理、有什么作用以及为什么3....
The bearing groove surface defects were difficult to identify, therefore an identification model named recursive feature elimination-Bayesian extreme gradient boosting tree (RFE-BXGBoost) was proposed for bearing ring groove surface defects. Firstly, feature derivatization was used ...
Experiments on long-term ozone concentration prediction on a global scale show that the prediction accuracy of the model after Bayesian optimized XGBoost-RFE feature selection is higher than that based on all features and on feature selection with Pearson correlation. Among the four prediction models,...
(area under curve,AUC)分别为:大车小标0.997,U/J型0.980,假绿通0.969,冲岗0.924.结果证明,基于RFE-OPTUNA-XGBoost的模型对于逃费模式识别的准确程度及各逃费模式的AUC值都更高.综上所述,提出的基于RFE-OPTUNA-XGBoost的高速公路逃费车辆逃费的识别模型能精准识别逃费模式.在实际应用中,对于高速公路管理部门展开稽查...
XGBoostRFE特征选择结冰预测风电机组长期在低温高湿的环境中运行会造成风机叶片的结冰,该现象会严重影响风机的发电效率.且不同风机的运行数据在不同工作情况下也存在很大的差异,首先去除掉风机数据中的无效值,接着对风机叶片正常与结冰数据之间的不平衡进行下采样处理,然后利用RFE算法挑选出与叶片结冰最有关联性的几个特...
Performance evaluation shows that an RFE-hybridized XGBoost model attained the highest classification accuracy, achieving 97.2% for FC1 and 97.1% for FC2. The model further optimized with a novel teaching learning-based population selection genetic algorithm (TLBPSGA) achieved a remarkable accuracy ...
ModelingTunable filtersOctane value (RON) is a factor which can represent the combustion performance, a decreased RON loss forecast model based on RFE, XGBoost and LightGBM was proposed in this paper. Firstly we used these 3 effective method to filter the feature subset out, each subset included...