RandomForest 중요도 시각화

RandomForest 모델 학습 코드

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

iris_data = load_iris() 

X = pd.DataFrame(iris_data.data, columns=iris_data.feature_names) 
y = pd.DataFrame(iris_data.target, columns=['class'])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

clf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42).fit(X_train, y_train)

y_pred = clf.predict(X_test)

score = accuracy_score(y_test, y_pred)

print('Random Forest Accuracy Score: ', score)

 

반응형

 

RandomForest 시각화

importances = clf.feature_importances_
sorted_importances = np.argsort(importances)
plt.bar(range(len(importances)), importances)
plt.xticks(range(len(importances)), X.columns, rotation=90)
plt.show()

RandomForest 시각화

 

 

RandomForest 설명 및 코드 리뷰는 아래 링크 참고

2023.08.21 - [scikit-learn] - Scikit-learn RandomForest 사용법