Answers.Body: <p>The term  <em><strong>generalization</strong></em>  refers to the model's ability to adapt and react appropriately to new, unpublished data that was drawn from the same distribution as the one used to build the model . In other words, generalization examines how well a model can digest new data and make correct predictions after being trained on a training set.
  <br></p>
  <p><strong>How well a model is able to generalize is key to its success.</strong></p>
  <p>If you train a model too well on the training data, it will be unable to generalize. In such cases, it will end up making wrong predictions when receiving new data.</p>
  
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Answers.Body: <p>When training machine to learn from data, it is typical withhold a set of data that the machine never sees. This withheld dataset is known as the test set. The performance of the learned model on the test set gives an idea of how well the model has learned the function that generated the data in the first place as opposed to overfitting to the training dataset. This measure of performance is typically referred to as generalization error and this is what is being referred to in the article.</p>
  
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Body: <ul>
  <li><a href="https://proceedings.neurips.cc/paper/2020/file/f3f27a324736617f20abbf2ffd806f6d-Paper.pdf" rel="nofollow noreferrer">Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning</a></li>
  </ul>
  <p>What does it mean by Generalization in this article?</p>
  
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Comments.Text: Before asking a question, do a little bit of research.  See https://ai.stackexchange.com/help/how-to-ask. The answer to this question can be easily found by pasting your question on [google.](https://www.google.com/search?q=machine+learning+Generalization)
Comments.UserDisplayName: nbro
Tags: <neural-networks><optimization>
Title: What does it mean by Generalization?
UserDisplayName: user366312
ThreadText: # What does it mean by Generalization?
  
  (Asked by: username_0 on 2022-02-08)
  
  * [Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning](https://proceedings.neurips.cc/paper/2020/file/f3f27a324736617f20abbf2ffd806f6d-Paper.pdf)
  
  What does it mean by Generalization in this article?
  
  **Tags:** neural-networks, optimization
  > **Comments:**
  > **username_1**: Before asking a question, do a little bit of research.  See https://ai.stackexchange.com/help/how-to-ask. The answer to this question can be easily found by pasting your question on [google.](https://www.google.com/search?q=machine+learning+Generalization)  
  
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  ## 2 answers
  
  ### Answer by username_2 on 2022-02-08. Score: 1 | Accepted answer
  
  The term ***generalization*** refers to the model's ability to adapt and react appropriately to new, unpublished data that was drawn from the same distribution as the one used to build the model . In other words, generalization examines how well a model can digest new data and make correct predictions after being trained on a training set.
  
  **How well a model is able to generalize is key to its success.**
  
  If you train a model too well on the training data, it will be unable to generalize. In such cases, it will end up making wrong predictions when receiving new data.
  
  
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  ### Answer by username_3 on 2022-02-08. Score: 0
  
  When training machine to learn from data, it is typical withhold a set of data that the machine never sees. This withheld dataset is known as the test set. The performance of the learned model on the test set gives an idea of how well the model has learned the function that generated the data in the first place as opposed to overfitting to the training dataset. This measure of performance is typically referred to as generalization error and this is what is being referred to in the article.
  
  
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