I'd like to ask everyone a question about how correlated features (variables) affect the classification accuracy of machine learning algorithms. With correlated features, I mean a correlation between them and not with the target class (i.e the perimeter and the area of a geometric figure or the level of education and the average income). In my opinion, correlated features negatively affect the accuracy of a classification algorithm, I'd say because the correlation makes one of them useless. Is it truly like this? Does the problem change with respect to the classification algorithm type? Any suggestions for papers and lectures are very welcome! Thanks