Comments (3)
it would be great to use data for simple prediction case by using .predict from compas.csv
https://github.com/fingoldin/pycorels/tree/master/examples/data
for all algorithms in you repo
like
M1
M2
QCBA
etc...
pls
from pyarc.
Hello Sandy,
some of the jupyter notebooks are only for my testing and developing purposes. But I think they should work if you change the working directory to some directory where you have the desired data.
I will definitely share some jupyter notebook or code snippets with usage examples, I've just been busy with schoolwork and work now so I didn't have much time to maintain this repository.
from pyarc.
Hello,
this was my mistake. I did not realize that the folders would not be in the repository when I created the example notebook. Before I get to correcting it - you can find the used data here. In the folds_discr
directory, you can find the discretized data and in the folds_nodiscr
directory are the undiscretized data.
from pyarc.
Related Issues (20)
- can you share more about algorithms used : for example about M1Algorithm, M2Algorithm HOT 3
- is it possible to set maximum number of rules like in pycorels?
- is it correct code: cars = generateCARs(txns_train , maxlen= 3, support= 0.1 , confidence = 0.2 )
- what can be done for unbalanced data? HOT 5
- how to get probability of classification reliability HOT 4
- if there is way to make sure which data used as target HOT 1
- predict_probability not = confidence ? HOT 4
- So how to find for each row Which elementary rule matches this row HOT 1
- may you clarify about support HOT 2
- if something can be done for unbalanced data?
- default class confidence is less than set by classier parameter confidence HOT 2
- Folders within qcba missing HOT 1
- Discretization HOT 2
- after installation pyarc-1.0.25 : builtins.ModuleNotFoundError: No module named 'pyarc.cba' HOT 8
- how to get best prediction( best rules) when number of rules is limited to target_rule_count value
- Default Class ID results in NaN
- Default example returns empty rule list HOT 1
- MacOS support? HOT 1
- theoretical issue HOT 1
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