Answers.Body: <p>The choice of label and features depends on your application. If you want to learn the location of expensive houses, then the latitude/longitude would be the label, and the other items the features. If you want to predict the house price from location and age of the house, then the median house value is the label.</p>
  <p>When deciding on the features, not all of them might be relevant. I would suggest doing a correlation analysis before choosing features, as you might find that age and value don't correlate, ie the age variable would contribute very little to the learning outcome.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-02-01T10:07:00.607Z
Answers.LastActivityDate: 2023-02-01T10:07:00.607Z
Answers.UserDisplayName: Oliver Mason
Body: <p><a href="https://developers.google.com/machine-learning/crash-course/california-housing-data-description" rel="nofollow noreferrer">https://developers.google.com/machine-learning/crash-course/california-housing-data-description</a></p>
  <p>I have also attached a snap but I am confused which column(s) is label? and which columns are features? According to my understanding, <strong>median_house_value</strong> is label while all other columns are features?</p>
  <p><a href="https://i.sstatic.net/g81QZ.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/g81QZ.jpg" alt="enter image description here" /></a></p>
  
Tags: <machine-learning>
Title: What is the 'label' column here in California housing example of Machine Learning?
UserDisplayName: DSP_CS
ThreadText: # What is the 'label' column here in California housing example of Machine Learning?
  
  (Asked by: username_0 on 2023-02-01)
  
  <https://developers.google.com/machine-learning/crash-course/california-housing-data-description>
  
  I have also attached a snap but I am confused which column(s) is label? and which columns are features? According to my understanding, **median\_house\_value** is label while all other columns are features?
  
  [![enter image description here](https://i.sstatic.net/g81QZ.jpg)](https://i.sstatic.net/g81QZ.jpg)
  
  **Tags:** machine-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-02-01. Score: 2
  
  The choice of label and features depends on your application. If you want to learn the location of expensive houses, then the latitude/longitude would be the label, and the other items the features. If you want to predict the house price from location and age of the house, then the median house value is the label.
  
  When deciding on the features, not all of them might be relevant. I would suggest doing a correlation analysis before choosing features, as you might find that age and value don't correlate, ie the age variable would contribute very little to the learning outcome.
  
  
  ---
  
  