Answers.Body: <p>Sorry but no one can give you a number. </p>
  
  <p>As stated already in the comment by Manngo, the general rule: is as much as you can. I have also seen the 10x number being thrown around, see <a href="http://fastml.com/how-much-data-is-enough/" rel="nofollow noreferrer">here</a>. According to this "rule", you need  roughly 10 times as many examples as there are degrees of freedom in your model.</p>
  
  <p>How much data you will need is something you can only answers to through empirical investigation. The amount of data you need to collect is influenced by the complexity of the data, the dimensionality of the data and the algorithm you intend to train(although this can be controlled somewhat through regulirazation, see this good blog post by <a href="https://jakevdp.github.io/blog/2015/07/06/model-complexity-myth/" rel="nofollow noreferrer">Jake Vanderplas</a> for more information). </p>
  
  <p><a href="https://stats.stackexchange.com/questions/51490/how-large-a-training-set-is-needed">This question</a>  might be of interest to you.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-10-10T13:49:26.603Z
Answers.LastActivityDate: 2017-10-10T14:16:22.747Z
Answers.LastEditDate: 2017-10-10T14:16:22.747Z
Answers.UserDisplayName: Tshilidzi Mudau
Body: <p>I'm trying to optimize a combination of 8 cards with 64 card characters. No repeats and order doesn't matter.</p>
  
  <p>n!/(n!(n-r)!) = 4,426,165,368 combinations</p>
  
  <p>I have everything set up, including the data scrapper. But I don't know how many games my machine needs to learn from to start seeing patterns. For example, in 14,000 games analyzed, only 834 decks had a certain character.</p>
  
  <p>Analyzing the next 7 cards from 834 decks is 621,216,192</p>
  
  <p>So I guess I need more data before reliable patterns emerge... but how much data? Thank you and god bless</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-08-09T16:50:27.707Z
Comments.Text: The general rule: as much as you can get.
Comments.UserDisplayName: Manngo
Tags: <machine-learning>
Title: How much data do I need to collect?
UserDisplayName: user6582640
ThreadText: # How much data do I need to collect?
  
  (Asked by: username_0 on 2017-08-08)
  
  I'm trying to optimize a combination of 8 cards with 64 card characters. No repeats and order doesn't matter.
  
  n!/(n!(n-r)!) = 4,426,165,368 combinations
  
  I have everything set up, including the data scrapper. But I don't know how many games my machine needs to learn from to start seeing patterns. For example, in 14,000 games analyzed, only 834 decks had a certain character.
  
  Analyzing the next 7 cards from 834 decks is 621,216,192
  
  So I guess I need more data before reliable patterns emerge... but how much data? Thank you and god bless
  
  **Tags:** machine-learning
  
  > **Comments:**
  > **username_1**: The general rule: as much as you can get.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-10-10. Score: 1
  
  Sorry but no one can give you a number.
  
  As stated already in the comment by username_1, the general rule: is as much as you can. I have also seen the 10x number being thrown around, see [here](http://fastml.com/how-much-data-is-enough/). According to this "rule", you need roughly 10 times as many examples as there are degrees of freedom in your model.
  
  How much data you will need is something you can only answers to through empirical investigation. The amount of data you need to collect is influenced by the complexity of the data, the dimensionality of the data and the algorithm you intend to train(although this can be controlled somewhat through regulirazation, see this good blog post by [Jake Vanderplas](https://jakevdp.github.io/blog/2015/07/06/model-complexity-myth/) for more information).
  
  [This question](https://stats.stackexchange.com/questions/51490/how-large-a-training-set-is-needed) might be of interest to you.
  
  
  ---
  
  