Examples of using Random forests in English and their translations into Chinese
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It consists of k-NN, Random Forest, and Naive Bayes base classifiers whose predictions are combined by Logistic Regression as a meta-classifier.
The research team used machine learning- specifically, the random forest technique- to determine which markers would be useful in diagnosing PTSD.
研究小组使用机器学习-特别是随机森林技术-来确定哪些标记物可用于诊断创伤后应激障碍。To say it in simple words: Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction.
定义随机森林建立多个决策树并将它们合并在一起以获得更准确和稳定的预测。Their natural extension is the Random Forest, which combines hundreds or thousands of trees to gain predictive power at the expense of interpretability.
决策树的自然扩展就是随机森林，它把成千上万的树合并到一起来获得更强的预测能力，代价是牺牲了解释能力。The Random Forest algorithm will take a random sample of 100 observations and five randomly chosen initial variables to build a CART model to work through.
随机森林算法将随机抽取一个包含100个观测值的样本和5个随机选择的初始变量，构建一个CART模型，然后执行。A best example of ensemble modelling is the random forest trees where many decision trees are used for predicting the results.
集成建模的最佳示例是随机森林，其中许多决策树用于预测结果。The random forest algorithm is used in a lot of different fields, like Banking, Stock Market, Medicine and E-Commerce.
随机森林算法可被用于很多不同领域，如银行，股票市场，医药和电子商务。After running various classifiers, we find that random forest, gradient boosting, and the ensemble models had the best performance comparatively.
运行各种分类器后，我们发现随机森林、梯度提升以及集成模型都表现出相对最好的性能。The number of features to be searched at each split point is specified as a parameter to the random forest algorithm.
在每个分割点处要搜索的特征的数量被指定为随机森林算法的参数。I used the randomForest() function from the R package"randomForest", which uses the non-parametric Breiman random forest algorithm to produce regression models.
我使用R语言“随机森林包”中的Randomforest()函数，该函数采用非参数Breiman随机森林算法来产生回归模型。The number of features to be searched at each split point is specified as a parameter to the random forest algorithm.
在每个分叉点要搜索的特征数量被指定为随机森林算法的一个参数。The drawback of the random forest method is that it is hard to understand why the computer reached any particular decision.
随机森林法的不足之处在于，难以理解计算机作出特定决策背后的原因。Parallel ensemble methods where the base learners are generated in parallel(e.g. Random Forest).
并行集成方法，其中参与训练的基础学习器并行生成（例如RandomForest）。The parallel ensemble methods where the base learners are generated in parallel(e.g. Random Forest).
并行集成方法，是基础学习对象并行生成的（例如RandomForest）。A random forest dissimilarity can be attractive because it handles mixed variable types very well, is invariant to monotonic transformations of the input variables, and is robust to outlying observations.
随机森林的不相似性度量之所以吸引人，是因为它能很好地处理混合变量类型，对输入变量的单调变换是不敏感的，而且对异常数据是鲁棒的。I built models using the most common data science algorithms: logistic regression, support vector machines(SVM), random forest and gradient boosted machines(GBM).
我使用最常见的数据科学算法来建立模型，包括：逻辑回归，支持向量机（SVM），随机森林和GradientBoostedMachines（GBM）。In recent years, machine learning approaches, including quantile regression forests(QRF), the cousins of the well-known random forest, have become part of the forecaster's toolkit.
近年来，机器学习方法，包括分位数回归森林算法（quantileregressionforests，QRF），著名的随机森林算法的表兄弟，已成为预测工具包的一部分。Random Forests.
随机森林.Random forests and GBDT.
随机森林与GBDT.Random forests and boosted trees.
随机森林和提升树.
