<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>

CC BY-SA 4.0
2019-01-02T11:30:37.533Z
2019-06-04T00:46:33.657Z
2019-06-04T00:46:33.657Z
nbro
<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>

<machine-learning><linear-regression><statistical-ai><features>
Does the correlation between inputs affect the model performance?
imtiaz ul Hassan
# 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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