azure - What is the role of feature type in AzureML?

Question

I want to know what is the difference between feature numeric and numeric columns in Azure Machine Learning Studio.

The documentation site states:

Because all columns are initially treated as features, for modules that perform mathematical operations, you might need to use this option to prevent numeric columns from being treated as variables.

But nothing more. Not what a feature is, in which modules you need features. Nothing.

I specifically would like to understand if the clear feature dropdown option in the fields in the edit metadata-module has any effect. Can somebody give me a szenario where this clear feature-operation changes the ML outcome? Thank you

According to the documentation in ought to have an effect:

Use the Fields option if you want to change the way that Azure Machine Learning uses the data in a model.

But what can this effect be? Any example might help

标签： azuremachine-learningazure-machine-learning-studiocode-documentation

Answer 1

正如您所怀疑的那样，将列设置为feature确实会产生影响，这实际上非常重要 - 在训练模型时，算法只会考虑带有feature标志的列，而实际上忽略了其他列。

例如，如果您有一个包含 、 和 列的数据集Feature1，Feature2并且Label您只想尝试Feature1，您将应用clear feature到该Feature2列（当然，要确保它Feature1具有feature标签集）。