Hello,
Maybe, it is an easy one and I just did not see it. Basically, I am running the machine learning app to predict a categorical field (OK/NOK).
It worked smoothly and I got some nice predictions. So far so good.
But now, on the hundreds of parameters that I added to predict this categorical field, how do I know which ones are the most important features. In Python with scikit learn, I will do something like that
importances = classifier.feature_importances_
indices = np.argsort(importances)
features = dataset.columns[0:26]
plt.figure(1)
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
However, i would prefer using the Splunk interface (my python skills are pretty limited), so my question, did I miss this option in the app? if not, can I use the results of splunk in the python script (e.g. how to get the features_importances_ as arguments for the script)?
Thanks
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