Answers.Body: <p>Non-correlation does not imply independence, that is, if two features are not correlated (i.e. zero correlation), it does not mean that they are independent. But (non-zero) correlation implies dependence (see <a href="https://stats.stackexchange.com/q/113417/82135">https://stats.stackexchange.com/q/113417/82135</a> for more details). So, if you have non-zero correlation between two features, it means they are dependent. If they are dependent, then one feature gives you information about the other and vice-versa: in a certain way, one of the two is, at least partially, redundant. </p>
  
  <p>Unnecessary features might not affect the performance (e.g. the accuracy) of a model. However, if you reduce the number of features, the learning process might actually be faster.</p>
  
  <p>You may want to try some <a href="https://en.wikipedia.org/wiki/Dimensionality_reduction" rel="noreferrer">dimensionality reduction</a> technique, in order to reduce the number of features.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-02T11:30:37.533Z
Answers.LastActivityDate: 2019-06-04T00:46:33.657Z
Answers.LastEditDate: 2019-06-04T00:46:33.657Z
Answers.UserDisplayName: nbro
Body: <p>I'm currently working on a regression problem and I have 10 inputs/attributes. </p>
  
  <p>What should I do if there are correlations between different features of the input data? Does the correlation between inputs affect the performance (e.g. accuracy) of the model?</p>
  
Tags: <machine-learning><linear-regression><statistical-ai><features>
Title: Does the correlation between inputs affect the model performance?
UserDisplayName: imtiaz ul Hassan
ThreadText: # Does the correlation between inputs affect the model performance?
  
  (Asked by: username_0 on 2019-01-02)
  
  I'm currently working on a regression problem and I have 10 inputs/attributes.
  
  What should I do if there are correlations between different features of the input data? Does the correlation between inputs affect the performance (e.g. accuracy) of the model?
  
  **Tags:** machine-learning, linear-regression, statistical-ai, features
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-01-02. Score: 5 | Accepted answer
  
  Non-correlation does not imply independence, that is, if two features are not correlated (i.e. zero correlation), it does not mean that they are independent. But (non-zero) correlation implies dependence (see <https://stats.stackexchange.com/q/113417/82135> for more details). So, if you have non-zero correlation between two features, it means they are dependent. If they are dependent, then one feature gives you information about the other and vice-versa: in a certain way, one of the two is, at least partially, redundant.
  
  Unnecessary features might not affect the performance (e.g. the accuracy) of a model. However, if you reduce the number of features, the learning process might actually be faster.
  
  You may want to try some [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) technique, in order to reduce the number of features.
  
  
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