Sklearn Random Forest Feature Importance - Plot Using Matplotlib

Python

A code snippet showing how to plot a horizontal bar chart showing the features importances for an Sklearn Random Forest model.

 1|  import matplotlib.pyplot as plt
 2|  from sklearn.ensemble import RandomForestRegressor
 3|  
 4|  # Step 1: Train Random Forest model
 5|  model = RandomForestRegressor()
 6|  model.fit(X_train, y_train)
 7|  
 8|  # Step 2: Plot feature importances
 9|  features = X_train.columns
10|  importance_values = model.feature_importances_
11|  
12|  plt.barh(y=range(len(features)),
13|           width=importance_values,
14|           tick_label=features)
15|  plt.show()
detro
Sklearn | Matplotlib | Random forest | Feature importance
Did you find this snippet useful?

Sign up for free to to add this to your code library

Remove Stop Words from Text in DataFrame Column
Python

Pandas | Nltk | Nlp

133
LightGBM Custom Loss Function
Python

LightGBM | Loss function

121
Tuning XGBoost Hyperparameters with Grid Search
Python

XGBoost | Tuning

116
How to Convert DataFrame Values Into Percentages
Python

Lambda | Percentages | Pipe

109
How to Scale Data Using Standard Scaler But Keep Column Names
Python

Scaler | Standard

78
LightGBM Hyperparameter Tuning with GridSearch
Python

Python | LightGBM | Hyperparameter tuning | Gridsearch

77
Calculating Root Mean Squared Error (RMSE) with Sklearn and Python
Python

Rmse

77
Dynamically Create Columns in Pandas Dataframe
Python

Pandas | Columns

61
How to Train a Catboost Classifier with GridSearch Hyperparameter Tuning
Python

Catboost | Hyperparameter tuning

60
Overwriting a Python Logging File Instead of Appending
Python

Logging

55