ensembles

ensembles的音标是["ensɪmblɪz]，基本翻译是“集体，团体；集合；组合”，速记技巧是：ensemble由ens+im+ble构成。

Ensemble这个词的词源可以追溯到拉丁语“ens”表示“存在”和“merere”表示“获取”，结合起来表示“聚集在一起的东西”。它的变化形式包括其过去分词形式“ensem莉”和现在分词形式“ensemble”，以及其名词形式“ensemble”。

相关单词：

ensemble（合唱团）：这个词可以指一个由多个成员组成的团体或组织，通常用于音乐表演或戏剧表演中。它也可以指一个由多个乐器或声音组成的整体效果。

ensembles（集体）：这个词可以指一群人聚集在一起，形成一个整体或团队。它也可以指一系列相关的物品或事件。

ensembles of ideas（思想集合）：这个词组可以用来描述一群相关或相似想法的聚集。

以上这些单词都有很强的关联性，因为它们都涉及到一群人或事物聚集在一起形成一个整体或团队。这些单词在英语中非常常见，并且在不同的语境中有着广泛的应用。

此外，ensemble还可以作为名词使用，表示一套服装或服饰的整体组合，也可以表示一个由多个部分组成的整体系统或结构。这个词在服装设计、建筑、音乐等领域有着广泛的应用。

总之，ensemble这个词及其变化形式和相关单词在英语中有着广泛的应用，它们不仅在语言学上有着重要的意义，而且在各个领域都有着重要的应用价值。

常用短语：

1. ensemble learning 集成学习

2. ensemble classifier 集成分类器

3. bagging ensemble 袋装集成

4. boosting ensemble 增强式集成

5. random ensemble 随机集成

6. voting ensemble 投票集成

7. stacking ensemble 堆叠集成

双语例句：

1. Ensemble learning has been widely used in machine learning tasks. (集成学习在机器学习任务中得到了广泛应用。)

2. The bagging ensemble method significantly improves the accuracy of classification. (袋装集成方法显著提高了分类的准确性。)

3. Boosting ensemble has achieved excellent results in many practical applications. (增强式集成在许多实际应用中取得了优异的效果。)

4. Random ensemble voting achieves good results in complex classification tasks. (随机集成投票在复杂的分类任务中取得了良好的效果。)

5. Stacking ensemble allows for more complex models to be combined effectively. (堆叠集成允许更复杂的模型得到有效的组合。)

6. The ensemble classifier is a powerful tool for improving the accuracy of machine learning models. (集成分类器是提高机器学习模型准确性的有力工具。)

7. Ensembles of models can provide a more reliable and robust approach to solving complex problems. (模型集合可以提供一种更可靠、更稳健的方法来解决复杂问题。)

英文小作文：

Ensemble Learning: A Powerful Tool for Improving Machine Learning Models

Ensemble learning is a powerful technique that combines multiple models to improve the accuracy and robustness of machine learning models. By combining different models, ensembles can effectively address the limitations of individual models and achieve better results in complex scenarios.

In this technique, we combine multiple models by using different strategies, such as bagging, boosting, random ensemble voting, stacking, and more. Each model in the ensemble has its own strengths and weaknesses, but when combined together, they can provide a more reliable and accurate prediction.

One of the most popular applications of ensemble learning is in classification tasks, where it has achieved excellent results in many practical applications. By combining multiple weak classifiers, ensemble classifiers can effectively address the issues of overfitting and improve the accuracy of predictions.

In addition to classification tasks, ensemble learning can also be applied to other machine learning tasks, such as regression and clustering. By using ensembles, we can improve the accuracy and robustness of these tasks and achieve better results in a wide range of applications. Therefore, ensemble learning is a powerful tool that can help us address complex problems and improve the accuracy of machine learning models.