python RandomForest

from sklearn import datasets

iris = datasets.load_iris()

x = iris.data #feature

y = iris.target # Label

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.3, random_state=88)

from sklearn.ensemble import RandomForestClassifier

#from sklearn.datasets import make_classification

clf = RandomForestClassifier(max_depth=2, random_state=0)

clf.fit(X_train, y_train)

pred = clf.predict(X_test)

from sklearn.metrics import accuracy_score

accuracy_score(pred,y_test)