Answers.Body: <p>Yes, you can and the answer is <strong>One-Class Classification</strong>. A well-written resource to understand is <a href="https://machinelearningmastery.com/one-class-classification-algorithms/" rel="nofollow noreferrer">this</a>.</p>
  
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Answers.CreationDate: 2020-09-15T09:05:49.260Z
Answers.Text: Thank you for your reply. I will check it out.
Answers.UserDisplayName: Niranjan Dindodi
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Answers.CreationDate: 2020-09-15T08:39:54.970Z
Answers.LastActivityDate: 2020-09-15T08:39:54.970Z
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Body: <p>I am using a dataset from Google which contains 1,27,000 data points on simulated concentrations of the atmosphere of exoplanets which can sustain life. So, the output label of all these data points is 1 i.e, probability of life existing there is 1. If I train my neural network on this data, and test it on data points with concentrations other than these, can I expect to get probability values at the output? Asking because the model knows no false labelled value.</p>
  
Tags: <neural-networks><deep-learning><classification><datasets><data-preprocessing>
Title: Can a neural network be trained on a dataset containing only values for true output for a classification problem?
UserDisplayName: Niranjan Dindodi
ThreadText: # Can a neural network be trained on a dataset containing only values for true output for a classification problem?
  
  (Asked by: username_0 on 2020-09-15)
  
  I am using a dataset from Google which contains 1,27,000 data points on simulated concentrations of the atmosphere of exoplanets which can sustain life. So, the output label of all these data points is 1 i.e, probability of life existing there is 1. If I train my neural network on this data, and test it on data points with concentrations other than these, can I expect to get probability values at the output? Asking because the model knows no false labelled value.
  
  **Tags:** neural-networks, deep-learning, classification, datasets, data-preprocessing
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-09-15. Score: 0 | Accepted answer
  
  Yes, you can and the answer is **One-Class Classification**. A well-written resource to understand is [this](https://machinelearningmastery.com/one-class-classification-algorithms/).
  
  > **Comments:**
  > **username_0**: Thank you for your reply. I will check it out.  
  
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