How Feature Columns Work In Tensorflow?

I read that feature columns in tensorflow are used to define our data but how and why? How do feature columns work and why they even exist if we can make a custom estimator without

Solution 1:

Broadly Speaking

This may be too general to answer. You may want to watch some videos or do more reading on machine learning, because this is a broad topic.

I will try to explain what features of data are used for.

A "feature" of the data is a meaningful variable that should separate two classes from each other. For example, if we choose the feature "weight", we can tell elephants apart from squirrels. They have very different weights, and our machine learning algorithm can learn to "understand" that an animal with a heavy weight is more likely to be an elephant than it is to be a squirrel. In a real scenario you would generally have more than one feature.

I'm not sure why you would say that Keras does not use features. They are a fundamental aspect of many classification problems. Some datasets may contain labelled data or labelled features, like this one: https://keras.io/datasets/#cifar100-small-image-classification

When we "don't use features", I think a more accurate way to state that would be that the data is unlabelled. In this case, a machine learning algorithm can still find relationships in the data, but without human labels applied to the data.

If you Ctrl+F for the word "features" on this page you will see places where Keras accepts them as an argument: https://keras.io/layers/core/

I am not a machine learning expert so if anyone is able to correct my answer, I would appreciate that too.

In Tensorflow

My understanding of Tensorflow's feature columns implementation in particular is that they allow you to cast raw data into a typed column that allow the algorithm to better distinguish what type of data you are passing. For example Latitude and Longitude could be passed as two numerical columns, but as the docs say here, using a Crossed Column for Latitude X Longitude may allow the model to train on the data in a more meaningful/effective way. After all, what "Latitude" and "Longitude" really mean is "Location." As for why Keras does not have this functionality, I am not sure, hopefully someone else can offer insight on this topic.