Lazy learning是什么意思 Lazy learning的读音、翻译、用法

'Lazy learning'这个词语来自于英语。它可以翻译为“懒惰学习”或“惰性学习”。它是一种机器学习方法，与传统的“急切学习”相反，懒惰学习是一种基于实例的学习方法，只有当需要时，才会对新数据进行分类或预测，这种方法在处理大量数据时非常高效。

以下是9个含有“lazy learning”的例句，使用英语并带有中文翻译：

1. Lazy learning algorithms do not generalize the training data until a predictive model is needed. (懒惰学习算法在需要预测模型之前不会将训练数据进行泛化。)

2. Lazy learning is beneficial for handling large datasets, as it doesn't require the computation of a model upfront. (懒惰学习对于处理大数据集非常有益，因为它不需要预先计算模型。)

3. One disadvantage of lazy learning is it can be computationally expensive when making predictions. (懒惰学习的一个缺点是当进行预测时可能需要消耗大量计算资源。)

4. The k-nearest neighbor algorithm is an example of lazy learning. (k最近邻算法是懒惰学习的一个例子。)

5. Lazy learning algorithms have a flexible decision boundary that adapts to the complexity of the data. (懒惰学习算法具有灵活的决策边界，能够适应数据的复杂性。)

6. The lazy learning approach is also known as instance-based learning. (懒惰学习方法也被称为基于实例的学习。)

7. Lazy learning algorithms can be used for both classification and regression problems. (懒惰学习算法既可以用于分类问题，也可以用于回归问题。)

8. A disadvantage of lazy learning is that it requires all the training data to be stored, which can be a problem for large datasets. (懒惰学习的一个缺点是需要存储所有的训练数据，对于大数据集可能会出现问题。)

9. Lazy learning algorithms are particularly useful for non-parametric problems, where the underlying data distribution is unknown. (懒惰学习算法在非参数问题上特别有用，其中基础数据分布是未知的。)