Answers.Body: <p>I think you need to look into semi-supervised learning, which combines supervised and unsupervised learning for problems where large labelled datasets are not available. To use this family of techniques, you need a small labelled dataset and a large unlabelled one.</p>
  <p>Create a dataset over good athletes, lets say the ones who are professional, and the traits you are going to use as input features. This will be your labelled positive dataset. For the one with negative labels, you can probably find data over people who failed at becoming professional.</p>
  <p>Now, you can use this dataset with a number of real, or generated, samples of people to train a model that can be used to make your desired predictions (use statistical data as reference when generating this dataset).</p>
  <p>The quality and size of both datasets will determine how long it'll take to train the model and how good the model can become.</p>
  <p>There are several approaches to semi-supervised learning. Read through <a href="https://arxiv.org/pdf/2103.00550.pdf" rel="nofollow noreferrer">this survey</a> to get started.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-11-04T12:31:45.100Z
Answers.LastActivityDate: 2021-11-04T12:31:45.100Z
Answers.UserDisplayName: Avatrin
Body: <p>I've got a task similar to the following:</p>
  <p>Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those individuals.</p>
  <p>So I was thinking of looking into assumptions/traits:
  Most of the NBA players are tall. If someone of a random amount of people is tall, it could be a good basketball player.
  In contrast, a tall person would not be a good jockey.
  The same goes for age - 3 years or 90 years old might not make a world-performing athlete, etc.</p>
  <p>How would I best build a dataset for this problem? Which features do I need to add to the dataset in order to make good predictions about athletic performance?</p>
  
Tags: <datasets><prediction><unsupervised-learning><data-science><feature-engineering>
Title: Generating a dataset from data with "assumed" lables
UserDisplayName: Chris
ThreadText: # Generating a dataset from data with "assumed" lables
  
  (Asked by: username_0 on 2021-10-28)
  
  I've got a task similar to the following:
  
  Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those individuals.
  
  So I was thinking of looking into assumptions/traits:
  Most of the NBA players are tall. If someone of a random amount of people is tall, it could be a good basketball player.
  In contrast, a tall person would not be a good jockey.
  The same goes for age - 3 years or 90 years old might not make a world-performing athlete, etc.
  
  How would I best build a dataset for this problem? Which features do I need to add to the dataset in order to make good predictions about athletic performance?
  
  **Tags:** datasets, prediction, unsupervised-learning, data-science, feature-engineering
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-11-04. Score: 1 | Accepted answer
  
  I think you need to look into semi-supervised learning, which combines supervised and unsupervised learning for problems where large labelled datasets are not available. To use this family of techniques, you need a small labelled dataset and a large unlabelled one.
  
  Create a dataset over good athletes, lets say the ones who are professional, and the traits you are going to use as input features. This will be your labelled positive dataset. For the one with negative labels, you can probably find data over people who failed at becoming professional.
  
  Now, you can use this dataset with a number of real, or generated, samples of people to train a model that can be used to make your desired predictions (use statistical data as reference when generating this dataset).
  
  The quality and size of both datasets will determine how long it'll take to train the model and how good the model can become.
  
  There are several approaches to semi-supervised learning. Read through [this survey](https://arxiv.org/pdf/2103.00550.pdf) to get started.
  
  
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