Plotting Feature Importance
Python
This is an example of using a function for generating a feature importance plot when using Random Forest, XGBoost or Catboost. This allows more intuitive evaluation of models built using these algorithms.
1| def plot_feature_importance(importance,names,model_type): 2| 3| #Create arrays from feature importance and feature names 4| feature_importance = np.array(importance) 5| feature_names = np.array(names) 6| 7| #Create a DataFrame using a Dictionary 8| data={'feature_names':feature_names,'feature_importance':feature_importance} 9| fi_df = pd.DataFrame(data) 10| 11| #Sort the DataFrame in order decreasing feature importance 12| fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)) 13| 14| #Define size of bar plot 15| plt.figure(figsize=(10,8)) 16| #Plot Searborn bar chart 17| sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names']) 18| #Add chart labels 19| plt.title(model_type + 'FEATURE IMPORTANCE') 20| plt.xlabel('FEATURE IMPORTANCE') 21| plt.ylabel('FEATURE NAMES') 22| 23| plot_feature_importance(rf_model.feature_importances_,train.columns,'RANDOM FOREST') 24| plot_feature_importance(xgb_model.feature_importances_,train.columns,'XG BOOST') 25| plot_feature_importance(cb_model.get_feature_importance(),train.columns,'CATBOOST')
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