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Is a correlation matrix meaningful for a binary classification task?
Suppose you have a number of features, say 20, for your binary classification task. It might be the case that out of these 20 features some features are highly correlated. That may introduce some sort of redundant features in your feature space, so you may start to figure out which features to drop and still achieve a good result.
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).
How are correlated features used in machine learning?
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 eh accuracy of a classification algorithm, I’d say because the correlation makes one of them useless.
How to calculate the correlation of all features?
Please see in the picture attached how the distribution would look like for one the features (from Weka). Any feedback is really appreciated. Correlation is not supposed to be used for categorical variables. For more explanation see here
Is it meaningful to calculate Pearson or Spearman correlation?
The Pearson and Spearman correlation are defined as long as you have some 0 s and some 1 s for both of two binary variables, say y and x. It is easy to get a good qualitative idea of what they mean by thinking of a scatter plot of the two variables. Clearly, there are only four possibilities (0, 0), (0, 1), (1, 0), (1,…
Can a Pearson coefficient be used for binary data?
Well correlation, namely Pearson coefficient, is built for continuous data. Thus when applied to binary/categorical data, you will obtain measure of a relationship which does not have to be correct and/or precise. There are quite a few answers on stats exchange covering this topic – this or this for example.
Which is the best way to model binary data?
On the whole, however, there will be better ways of thinking about such data, depending largely on the main motive for such a study. For example, the fact that correlations make much sense does not mean that linear regression is a good way to model a binary response.