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I am utilizing the ECM classifier as my unsupervised classifier for my problem but I keep getting error while calling them that I do not understand why:
ecm.fit(df_feature_vectors)
log_m_probablity = ecm.log_m_probs
which gives the following error: ValueError: Expected input with 6 features, got 5 instead
while my feature_vector has only 5 features. and also upon using ecm.prob, got the following error:
ValueError: Expected input with 11 features, got 5 instead
Interestingly, every time, I run this, it expects 5 more features like expected 16 features, 21 features, .....
what is the solution in order to use these methods such as log_m_probs, prob, log_u_probs, etc.???
Also one more question regarding this is that as I was employing the prodict method: links_pred = ecm.predict(df_feature_vectors) where df_feature_vectors = comparer.compute(All_Index_Pairs, df), it threw error such that the labels had to be either one or zero and I had to use binarizer to make the labels either one or zero in order to avoid the error. why can't the labels be between zero and one?
The text was updated successfully, but these errors were encountered:
Hi
I am utilizing the ECM classifier as my unsupervised classifier for my problem but I keep getting error while calling them that I do not understand why:
ecm.fit(df_feature_vectors)
log_m_probablity = ecm.log_m_probs
which gives the following error: ValueError: Expected input with 6 features, got 5 instead
while my feature_vector has only 5 features. and also upon using ecm.prob, got the following error:
ValueError: Expected input with 11 features, got 5 instead
Interestingly, every time, I run this, it expects 5 more features like expected 16 features, 21 features, .....
what is the solution in order to use these methods such as log_m_probs, prob, log_u_probs, etc.???
Also one more question regarding this is that as I was employing the prodict method: links_pred = ecm.predict(df_feature_vectors) where df_feature_vectors = comparer.compute(All_Index_Pairs, df), it threw error such that the labels had to be either one or zero and I had to use binarizer to make the labels either one or zero in order to avoid the error. why can't the labels be between zero and one?
The text was updated successfully, but these errors were encountered: