Comments (3)
M1 and M2 are two algorithmic realizations of CBA, as described here: https://www.aaai.org/Papers/KDD/1998/KDD98-012.pdf
Both algorithms provide the same results differ in speed.
In this paper http://ceur-ws.org/Vol-2204/paper6.pdf, we found that M1 implementation from pyARC is faster than the M2 implementation.
from pyarc.
I see
Thanks for references
Will try to read
Hopefully will be able to understand ideas
Since I only started to learn this great techniques
By the way do you have some simple itroductury level description
May be something like material for students?
from pyarc.
Maybe you can refer to this tutorial: From association rules to interpretable
classification models - a tutorial
from pyarc.
Related Issues (20)
- 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
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- how to get best prediction( best rules) when number of rules is limited to target_rule_count value
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- theoretical issue HOT 1
- can you share some simple example how to user .predict 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 )
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from pyarc.