Quick overview of how to use a Random Forest Classifier using Scitkit Learn

sklearn_random_forest.py

# A breif overview of how to create a Random Forest Classifier using Scikit-Learn. For a more detailed breakdown and 
# an overview of what a Random Forest is, you can find the original post here: http://timcrammond.com/blog/what-is-random-forest/
from sklearn import datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

wine = datasets.load_wine()

X = wine.data
y = wine.target

y = wine.target

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, y_train)

y_predict = random_forest.predict(X_test)
print(f'Our Random Forest Classifier is {metrics.accuracy_score(y_test, y_predict)} accurate')