No variance in confusion matrix output
LeoKrak
New Altair Community Member
Hi,
If anyone is available now, maybe i can go in further details but in brief my team and I are building a model that should predict who among a list of
previous donators would donate for the next campaign, based on a database with previous donators and their characteristics.
We seem to do everything right but I think we face an overfitting issue because we always get the same results in the confusion matrix no matter how many tries we make.
Also, the model has extreme poor performances but what is really disturbing is that the output doesnt vary at all.. Any help ?
If anyone is available now, maybe i can go in further details but in brief my team and I are building a model that should predict who among a list of
previous donators would donate for the next campaign, based on a database with previous donators and their characteristics.
We seem to do everything right but I think we face an overfitting issue because we always get the same results in the confusion matrix no matter how many tries we make.
Also, the model has extreme poor performances but what is really disturbing is that the output doesnt vary at all.. Any help ?
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Answers
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Hello @LeoKrak
Your data sets seem to be highly imbalanced with more than 3700 samples belong to one class and just 54 to another. You need to try data preprocessing methods like sampling (on training side) or weighting.
Also, try to do some feature selection and tune your hyperparameters of the model, you can do so by using Optimize parameter (Grid) operator.4 -
Thanks a lot I'll try this tomorrow morning ! Can I come back to you if I have any other questions ?1
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Sure, we are here to help0
