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Describe the bug
When i run below code with my data :
# LR Filter with order 2`method='LR'f_imp=filter_method.get_importance(data_train, data_train.columns.tolist(), y_name='label', method=method, experiment_group_column='ff_rate', control_group=0, treatment_group=1, order=2)
f_imp.head()
hi @xhxt2008 , just looking at this quickly, but here are two potential reasons behind this:
are any of your X variables constant?
do you have a very small sample size?
From my understanding of the code, it looks like it's attempting to fit an LR model to each of the features. Either of the two cases above could cause the matrix to be ill-conditioned or singular.
The sample size doesn't look inherently "small", but it does depend on the distributions of your features. If any feature is constant, it will result in a singular matrix which can't be inverted. I think this could also happen if your features are highly skewed (so maybe not constant, but "almost constant").
Describe the bug
When i run below code with my data :
An Error occured:
umath_linalg.inv
got a singular matrix.The text was updated successfully, but these errors were encountered: