Claudio Sartori bladerun16
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| import itertools | |
| def jaccard(labels1, labels2): | |
| """ | |
| Computes the Jaccard similarity between two sets of clustering labels. | |
| The value returned is between 0 and 1, inclusively. A value of 1 indicates | |
| perfect agreement between two clustering algorithms, whereas a value of 0 | |
| indicates no agreement. For details on the Jaccard index, see: | |
| http://en.wikipedia.org/wiki/Jaccard_index |
fmarthoz
/ feature_selection_kmeans
Last active
December 16, 2022 09:16
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| scaler = StandardScaler() | |
| df=scaler.fit_transform(df) | |
| kmeans_kwargs = {"init": "random","n_init": 20,"max_iter": 1000,"random_state": 1984} | |
| cut_off=0.5 | |
| maxvars=3 | |
| kmin=2 | |
| kmax=8 | |
| cols=list(df.columns) |