Answers.Body: <blockquote>
  <p>Specifically, what with experience E means? Does it mean with more data?</p>
  </blockquote>
  <p>You can think of an experience as information. These are observations of any real world system outside of the program. You can also view it as a reduction in entropy.</p>
  <blockquote>
  <p>Moreover, improves means that we compare with something. In that case, we compare the performance P with what?</p>
  </blockquote>
  <p>See the original quote: &quot;improves with experience E.&quot; The word 'with' here is a  common way to establish a mathematical relationship between P and T. It can be interpreted as (greater E) implies (greater P). This specifically though means that P is monotone increasing with respect to E, but it's also possible that he means P is only asymptotically increasing, which has a more complicated definition.</p>
  <p>What Mitchell is saying overall can be interpreted simply as: when we decrease entropy (by making observations), the performance metric increases.</p>
  <p>In my opinion, this is not really a rigorous definition of machine learning. It is just an informal description that fits a number of possible definitions of machine learning.</p>
  
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Answers.CreationDate: 2024-07-22T09:41:28.627Z
Answers.Text: Still struggling to understand the "improves with experience E". If we remove some data (experience) but the performance increases, would this be considered learning? If we stick to the interpretation "improves with experience E" as "as (greater E) implies (greater P)", the certainly there is no learning at all besides the fact that the performance increased.
Answers.UserDisplayName: Antonios Sarikas
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Answers.CreationDate: 2024-08-04T23:10:31.123Z
Answers.Text: @adosar this quoted Tom's informal definition of ML can be easily seen in the non-parametric models such as KNN, more experience means more training data points, if you remove some training data, then the ML model performance would decrease compared to its value before the said removal. For most parametric ML models, removal of training data or environment interactions *after* training would simply have no effect for any performance P.
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Answers.UserDisplayName: Venna Banana
Body: <p>I quote the well known definition:</p>
  <blockquote>
  <p>A computer program is said to learn from experience E with respect to some class of tasks T and some performance measure P, if its performance on T, as measured by P, improves with experience E.</p>
  </blockquote>
  <p>I have a hard time to digest what this definition exactly means.</p>
  <p>Specifically, what <strong>with experience E</strong> means? Does it mean <strong>with more data</strong>?</p>
  <p>Moreover, <strong>improves</strong> means that we compare with something. In that case, we compare the performance P with what?</p>
  
Tags: <machine-learning><definitions>
Title: How to interpret Tom Mitchell's definition of machine learning?
UserDisplayName: Antonios Sarikas
ThreadText: # How to interpret Tom Mitchell's definition of machine learning?
  
  (Asked by: username_0 on 2023-10-20)
  
  I quote the well known definition:
  
  > A computer program is said to learn from experience E with respect to some class of tasks T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
  
  I have a hard time to digest what this definition exactly means.
  
  Specifically, what **with experience E** means? Does it mean **with more data**?
  
  Moreover, **improves** means that we compare with something. In that case, we compare the performance P with what?
  
  **Tags:** machine-learning, definitions
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-10-20. Score: 2
  
  > Specifically, what with experience E means? Does it mean with more data?
  
  You can think of an experience as information. These are observations of any real world system outside of the program. You can also view it as a reduction in entropy.
  
  > Moreover, improves means that we compare with something. In that case, we compare the performance P with what?
  
  See the original quote: "improves with experience E." The word 'with' here is a common way to establish a mathematical relationship between P and T. It can be interpreted as (greater E) implies (greater P). This specifically though means that P is monotone increasing with respect to E, but it's also possible that he means P is only asymptotically increasing, which has a more complicated definition.
  
  What Mitchell is saying overall can be interpreted simply as: when we decrease entropy (by making observations), the performance metric increases.
  
  In my opinion, this is not really a rigorous definition of machine learning. It is just an informal description that fits a number of possible definitions of machine learning.
  
  > **Comments:**
  > **username_0**: Still struggling to understand the "improves with experience E". If we remove some data (experience) but the performance increases, would this be considered learning? If we stick to the interpretation "improves with experience E" as "as (greater E) implies (greater P)", the certainly there is no learning at all besides the fact that the performance increased.  
  > **username_2**: @adosar this quoted Tom's informal definition of ML can be easily seen in the non-parametric models such as KNN, more experience means more training data points, if you remove some training data, then the ML model performance would decrease compared to its value before the said removal. For most parametric ML models, removal of training data or environment interactions *after* training would simply have no effect for any performance P.  
  
  ---
  
  