random-forest-importances
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parrt
Code to compute permutation and drop-column importances in Python scikit-learn models
I am using a dataset to compute feature importance using permutation. Have checked results with R implementation, I am getting non zero var importance. What could be the reason? Here...
Just tested the code from index page, some of my features have negative values, does it mean reverse-related to target feature or something else? Thank you.
How many other forms of feature importance are there, and how are they different from one another? - Shapley-based - https://github.com/slundberg/shap - https://github.com/iancovert/sage - LOFO https://github.com/aerdem4/lofo-importance - LIME https://github.com/marcotcr/lime -...
I got an error running "permutation-importances-classifier", “forest” seems to be updated to “_forest” in sklearn. I changed "from sklearn.ensemble.forest import _generate_unsampled_indices" to "from sklearn.ensemble._forest import _generate_unsampled_indices" and it worked fine....
When I use "feature_dependence_matrix" function to get the dependency of each independent variables, the values change every time I run the code. Specifying the number of random_state only allow me...
File "D:\python\lib\site-packages\rfpimp.py", line 143, in importances features = X_valid.columns.values AttributeError: 'numpy.ndarray' object has no attribute 'columns'
Continuation of PR #20 , addressing Issue #19
Wanted to suggest parallelizing the oob importance calculation in order to speed it up, since the importances can be calculated independently for each feature. In my use case, I saw...
see [Permutation importance: a corrected feature importance measure](https://academic.oup.com/bioinformatics/article/26/10/1340/193348) and [kaggle blog](https://www.kaggle.com/ogrellier/feature-selection-with-null-importances)
