Class imbalance can be an issue for algorithms, but decision forests
should in general cope reasonably well with imbalanced classes. By
default, positive and negative classes are treated 'equally' however,
and that may not reflect reality in some cases. Upsampling the
under-represented case is a
Hi All,
Here I have lot of data with around 1,000,000 rows, 97% of them are negative
class and 3% of them are positive class . I applied Random Forest algorithm to
build the model and predict the testing data.
For the data preparation,i. firstly randomly split all the data as training
data
