random forest regressor python
from sklearn.ensemble import RandomForestRegressor
clf = RandomForestRegressor(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))random forest regressor python
from sklearn.ensemble import RandomForestRegressor
clf = RandomForestRegressor(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))Random forest classifier python
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# Creating a bar plot
sns.barplot(x=feature_imp, y=feature_imp.index)
# Add labels to your graph
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features")
plt.legend()
plt.show()Copyright © 2021 Codeinu
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