Should I drop correlated features?
Correlated features will not always worsen your model, but they will not always improve it either. There are three main reasons why you would remove correlated features: Make the learning algorithm faster.
Where do we use correlated variables?
For example, assume the size of a house and the number of bedrooms are correlated. When predicting the cost of a house using these two predictors, one of them can be dropped because they are both providing a lot of the same information.
Can a feature be negatively correlated with the target?
A large negative correlation is just as useful as a large positive correlation. The only difference is that for a positive correlation, as the feature increases, the target will increase. For a negative correlation, as the feature decreases, the target will increase. Any model you choose should be able to handle the correlation sign (+/-).
What does low correlation with target variable mean?
That’s problematic enough to start. Low correlation means there’s no linear relationship; it doesn’t mean there’s no information in the feature that predicts the target. I think you’re really looking for mutual information, in this case between continuous and categorical variables.
What does low correlation in a feature mean?
Low correlation means there’s no linear relationship; it doesn’t mean there’s no information in the feature that predicts the target. I think you’re really looking for mutual information, in this case between continuous and categorical variables. (I assume your other inputs are continuous?)
How can I check the correlation between…?
Now using some machine learning on this data is not likely to work. There just is not sufficient data to extract some relevant information between your large number of features and the loan amount. You need at at least 10 times more instances than features in order to expect to get some good results.