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基于随机森林分类对水下导航适配区分类预测
首发时间：2024-05-16
摘要：采用插值方法对附件一的数据进行精细化处理，依据重力异常变化的幅度和范围，采用聚类分析中k-means算法可得两类。然后采用分层模型中的随机森林模型。其在测试数据上没有产生任何误分类：假正例和假反例的总数是0，并对于测试集上对类别1和类别2的预测完全正确。相较于其它两种模型，随机森林模型更准确、误差更小、更适用于水下导航适配区分类预测。随机森林模型在水下导航适配区预测和分析中可以提供可靠的预测能力，并有助于改善水下导航的安全性和效率。但需要注意，模型的性能取决于数据的质量和特征工程的质量，因此数据采集和前期准备工作非常关键。此外，模型的应用也需要结合专业的领域知识和实际场景来做出决策。
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classification prediction of underwater navigation fitness zones based on random forest classification
abstract：the interpolation method was used to refine the data in annex i. based on the magnitude and range of the gravity anomaly changes, two categories could be obtained using the k-means algorithm in cluster analysis. then the random forest model in hierarchical modeling was used. it did not produce any misclassification on the test data: the total number of false positives and false negatives was 0, and was completely correct for the predictions of categories 1 and 2 on the test set. compared to the other two models, the random forest model is more accurate, less error-prone, and more suitable for underwater navigation fitness zone classification prediction. the random forest model can provide reliable prediction capability in underwater navigation fitness zone prediction and analysis, and help to improve the safety and efficiency of underwater navigation. however, it is important to note that the performance of the model depends on the quality of the data and the quality of the feature engineering, so data collection and pre-preparation are critical. in addition, the application of the model requires a combination of specialized domain knowledge and real-world scenarios to make decisions.
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