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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?

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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 technique, in order to reduce the number of features.

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