What is the translation of " RANDOM FORESTS " in Chinese?

• 随机森林

Examples of using Random forests in English and their translations into Chinese

And relies on easily understood random forests instead of completely opaque deep neural nets.
依赖易于理解的随机森林，而不是完全不透明的深度神经网络。

To estimate the true\(f\), we use different methods, like linear regression or random forests.
为了估计真正的f,我们会使用线性回归或者随机森林等不同的方法。

Then there was boosting, random forests, and so on, till the return of neural networks.
然后有boosting，randomforests，等等，直到回到神经网络。

We note that Industry Data Scientists are more likely to use Regression, Visualization, Statistics, Random Forests, and Time Series.
我们发现，产业数据科学家更喜欢用回归、可视化、统计、随机森林和时间序列。

Random forests can be used to rank the importance of variables in a regression or classification problem in a natural way.
随机森林天然可用来对回归或分类问题中变量的重要性进行排序。

Jared: The most popular algorithms and models I have seen lately are the Elastic Net, Decision Trees and Random Forests.
Jared：最近看到的最流行的算法和模型包括弹性网络（ElasticNet）、决策树和随机森林。

Similar to ordinary random forests, the number of randomly selected features to be considered at each node can be specified.
与普通的随机森林相似，可以指定每个节点要选择的特征的个数。

We note that Industry Data Scientists are more likely to use Regression, Visualization, Statistics, Random Forests, and Time Series.
我们注意到，产业数据科学家们更倾向于用回归，可视化，统计，随机森林和时间序列。

Black box(opaque) models: Deep neural networks, random forests, and gradient boosting machines can be considered in this category.
黑匣子(不透明)模型：深度神经网络、随机森林和梯度增强机器可以在此类别中考虑。

We note that Industry Data Scientists are more likely to use Regression, Visualization, Statistics, Random Forests, and Time Series.
我们注意到，产业数据科学家们更倾向于使用回归、可视化、统计、随机森林和时间序列。

Models like random forests are less interpretable, More suitable“ machine learning” Description, But deep learning is hard to explain.
像随机森林这种模型可解释性稍差，更适合“机器学习”的描述，而深度学习等方法则难以解释。

Another disadvantage is that they easily overfit, but that's where ensemble methods like random forests(or boosted trees) come in.
另一个缺点是，容易过拟合，但这也正是诸如随机森林（或提高树）之类的集成方法的切入点。

In random forests, each tree in the ensemble is built from a sample drawn with replacement(i.e. a bootstrap sample) from the training set.
在随机森林中，集成的每一棵树都是从训练集中用替换（例如，引导样本）绘制样本构建的。

Within this comparison, the best-performing prediction methods on the training data turn out to be the ranking methods and the random forests.
在这个比较中，在训练数据中表现最好的预测方法是排名方法和随机森林。

In random forests, each tree in the ensemble is built from a sample drawn with replacement(i.e. a bootstrap sample) from the training set.
在随机森林中，集成中的每棵树都是由从训练集中抽取的样本（即bootstrap样本）构建的。

Here, based on our specific machine learning problems, we apply useful algorithms like regressions, decision trees, random forests, etc.
这里，基于具体的机器学习问题，我们要应用有效的算法，如回归，决策树，随机森林等。

Tree-Based algorithms: Tree-based algorithms such as decision trees, Random Forests, and Boosted trees are used to solve both classification and regression problems.
基于树的算法：决策树、随机森林和提升树等基于树的算法用于解决分类和回归问题。

SciKit-Learn is equipped with a variety of ML models including linear and logistic regressors, SVM classifiers, and random forests.
Scikit-learn配备了各种ML模型，包括线性和逻辑回归器、SVM分类器和随机森林。

It supports smarter application such as deep learning, gradient boosting, random forests, generalized linear modeling(I. e logistic regression, Elastic Net) and many more.
它支持更智能化的应用，比如深度学习、梯度提升、随机森林、广义线性建模(比如逻辑回归和弹性网络)以及其他更多。

We note that Industry Data Scientists are more likely to use Regression, Visualization, Statistics, Random Forests, and Time Series.
我们注意到产业界数据科学家更倾向使用回归算法、可视化、统计算法、随机森林算法和时间序列。

In machine learning, we often use“black-box” methods-[classification algorithms called] random forests, or deeper learning approaches.
在机器学习中，我们经常使用“黑盒子”方法-[也叫做分类算法]、随机森林或更深入的学习方法。

Deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
由于深度学习如此受欢迎，我们将研究随机森林、AdaBoost和深度学习神经网络之间的一些有趣的共同点。

A Random Forest.
一随机森林.

Random Forest based Classification and Regression.
基于随机森林的分类与回归.

Product A Random Forest.
产品A随机森林.

In this case, let us discuss Random Forest.
在这种情况下，让我们讨论随机森林。

Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems.
决策树、随机森林、梯度增加等方法被广泛用于各种数据科学问题。