How are correlated features affect the classification accuracy?

How are correlated features affect the classification accuracy?

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 to eliminate features with a high correlation?

4.1 Greedy Elimination The idea of this approach is to iteratively elimnate features with respect to their correlation to other features. Therefore, the feature pair with the highest absolute correlation coefficient is selected. The feature of this pair which has the lower correlation with the passengers’ survival is eliminated.

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 handle correlation features in Kaggle kernel?

The produced data set contains a lot of strongly correlated features. In this kernel I analyse these correlation. Furthermore, I implement different strategies to reduce the number of features and test these strategies with respect to the achievable prediction accuracy of different classifiers.

What is the strength of a correlation coefficient?

The strength of relationship can be anywhere between −1 and +1. The stronger the correlation, the closer the correlation coefficient comes to ±1. If the coefficient is a positive number, the variables are directly related (i.e., as the value of one variable goes up, the value of the other also tends to do so).

What happens if there are too many correlated features?

For statistical methods such as Gaussian maximum likelihood, having too many correlated features relative to the training sample size will render the classifier unusable in the original feature space (the covariance matrix of the sample data becomes singular).