In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
在统计建模中,有很多分类器构建算法,每个算法构造一组不同的关于数据的假设集合。
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
为了获得最好的模型性能,挑选做出最合适假设的建模算法—而不只是选择你最熟悉那个算法,是很重要的。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
随着数据集的数据量和维数的增加,建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
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