Re: [scikit-learn] Imblearn: SMOTENC

As stated in the doc, categorical_features are the indices of the categorical column and not the name of the columns. This is similar to the one hot encoder API. Sent from my phone - sorry to be brief and potential misspell. ___ scikit-learn

Re: [scikit-learn] Imblearn: SMOTENC

Dear Mr. Lemaitre Thanks a lot for sharing your time and knowledge. Unfortunately, it throws the following error: Traceback (most recent call last): 119 File "D:/mifs-master_2/MU/learning-from-imbalanced-classes-master/learning-from-imbalanced-classes-master/continuous/Final

[scikit-learn] cluster.affinity_propagation doesn't accept sparse?

It appears affinity propagation would appear to accept sparse similarity input: X = check_array(X, accept_sparse='csr') But if I try it, I get: ~/.local/lib/python3.7/site- packages/sklearn/cluster/affinity_propagation_.py in affinity_propagation(S, preference, convergence_iter,

[scikit-learn] affinity propagation not giving desired answer

I am not too familiar with affinity propagation, but just trying it out. The problem is to cluster using a distance metric that is euclidean distance but with a limit. When the distance is greater than some threshold than the metric is -Inf. In other words, a point can be accepted into a

[scikit-learn] Affinity Propagation is the best algo for without choosing the number of cluster?

I search for clustering algo to cluster into groups without considering about number of groups. I found AP algo which needn't choose the number of clusters. In my experiments, AP cluster well without choosing any parameters. But I'm not sure any corner case which will caused clustering worse.