What Are Features And Labels In Machine Learning? | Codeing School

The label is the final choice, such as dog, fish, iguana, rock, etc. Once you've trained your model, you will give it sets of new input containing those features; it will return the predicted "label" (pet type) for that person. Feature: In Machine Learning feature means a property of your training data.

What Are Features And Labels In Machine Learning? | Codeing School

Features:

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.[1] Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of the explanatory variable used in statistical techniques such as linear regression.

Labels:

The output you get from your model after training it is called label.

Suppose you fed above dataset to some algorithm and generates a model to predict gender as Male or Female, In the above model you pass features like age, height etc.

So after computing, it will return the gender as Male or Female. That is called a Label

Example:

See the picture. If I say, what is it Cat or Dog?

 Your answer is Cat. How you say that this a cat, not a dog. Probably you see the ears, eyes, mouth. Then you say that this is a cat.

This is called Features and your answer is this is a cat, this conclusion or result, this is called label.

In the ML model, we insert Features and get Label.

In ML Algo Features and Labels are,

F1	F2	F3	F4	LABELS
EYE	EAR	MOUTH	NOSE
1(TRUE)	1(TRUE)	1(TRUE)	1(TRUE)	CAT

F1 = Features.

In a DataSet, Features are in Millions