Mapping column names to random forest feature importances

Question

I am trying to plot feature importances for a random forest model and map each feature importance back to the original coefficient. I\'ve managed to create a plot that shows the

Answer 1

Another simple way to get a sorted list

importances = list(zip(xgb_classifier.feature_importances_, df.columns))
importances.sort(reverse=True)

Next code adds a visualization if it's necessary

pd.DataFrame(importances, index=[x for (_,x) in importances]).plot(kind = 'bar')

Answer 2

A sort of generic solution would be to throw the features/importances into a dataframe and sort them before plotting:

import pandas as pd
%matplotlib inline
#do code to support model
#"data" is the X dataframe and model is the SKlearn object

feats = {} # a dict to hold feature_name: feature_importance
for feature, importance in zip(data.columns, model.feature_importances_):
    feats[feature] = importance #add the name/value pair 

importances = pd.DataFrame.from_dict(feats, orient='index').rename(columns={0: 'Gini-importance'})
importances.sort_values(by='Gini-importance').plot(kind='bar', rot=45)

Answer 3

I use a similar solution to Sam:

import pandas as pd
important_features = pd.Series(data=brf.feature_importances_,index=x_dummies.columns)
important_features.sort_values(ascending=False,inplace=True)

I always just print the list using print important_features but to plot you could always use Series.plot

Answer 4

It is simple, I plotted it like this.

feat_importances = pd.Series(extraTree.feature_importances_, index=X.columns)
feat_importances.nlargest(15).plot(kind='barh')
plt.title("Top 15 important features")
plt.show()