Answers.Body: <p>You can't really know for sure, unless you know all possible values that can be sampled from the distribution or know the distribution -  in that case, you don't need machine learning and you can just use your known distribution.</p>
  <p>However, you can compute some estimates of the number of samples you will need to achieve a certain accuracy with a certain probability (see <strong>PAC learning</strong>), but nobody applies this in practice and, sadly, almost nobody cares about the theory and science, but just about brute-forcing, with the hope that it will someday magically work. So, in practice, people will just use rules of thumb, like you need as many samples as number of parameters or whatever. Just some random suggestions based on 1-2 experiments, as if an example would prove the theorem.</p>
  
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Body: <p>Suppose I have a clean (no outliers and normalized) dataset for training a neural network.</p>
  <p>The training process is expected to take almost a week. So, before I start training, I want to know if this data is good enough to achieve <strong>at least</strong> <code>x%</code> accuracy.</p>
  <p>How do I know this dataset contains enough features, values, labels, rows, columns, etc. to properly train the neural network?</p>
  
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Comments.Text: How many features does your dataset have, aprox?
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Comments.Text: @PeymanMohamadpour, nine features.
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Tags: <neural-networks><datasets>
Title: How do I know that my dataset is good enough for training a neural network?
UserDisplayName: user366312
ThreadText: # How do I know that my dataset is good enough for training a neural network?
  
  (Asked by: username_0 on 2024-03-15)
  
  Suppose I have a clean (no outliers and normalized) dataset for training a neural network.
  
  The training process is expected to take almost a week. So, before I start training, I want to know if this data is good enough to achieve **at least** `x%` accuracy.
  
  How do I know this dataset contains enough features, values, labels, rows, columns, etc. to properly train the neural network?
  
  **Tags:** neural-networks, datasets
  
  > **Comments:**
  > **username_0**: https://datascience.stackexchange.com/q/128310/115793  
  > **username_0**: https://stats.stackexchange.com/q/642686/109372  
  > **username_1**: How many features does your dataset have, aprox?  
  > **username_0**: @PeymanMohamadpour, nine features.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-03-15. Score: 1
  
  You can't really know for sure, unless you know all possible values that can be sampled from the distribution or know the distribution - in that case, you don't need machine learning and you can just use your known distribution.
  
  However, you can compute some estimates of the number of samples you will need to achieve a certain accuracy with a certain probability (see **PAC learning**), but nobody applies this in practice and, sadly, almost nobody cares about the theory and science, but just about brute-forcing, with the hope that it will someday magically work. So, in practice, people will just use rules of thumb, like you need as many samples as number of parameters or whatever. Just some random suggestions based on 1-2 experiments, as if an example would prove the theorem.
  
  
  ---
  
  