3.Feature importance

from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt

X, y = datasets.load_iris(return_X_y=True)
model = RandomForestClassifier(random_state=0, n_jobs=-1).fit(X, y)
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
names = [datasets.load_iris().feature_names[i] for i in indices]

plt.bar(range(X.shape[1]), importances[indices])
plt.xticks(range(X.shape[1]), names, rotation=90)
plt.title("Feature Importance")
plt.show()