from sklearn import datasets
iris = datasets.load_iris();

from sklearn.decomposition import PCA
dim = 2

pca = PCA(n_components=dim, whiten=True)
data_reduced = pca.fit(iris.data).transform(iris.data)

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(data_reduced, iris.target, test_size=0.7)

# Random Forest
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)
clf_rf = rf.fit(X_train, Y_train)
print("Acurracy: ", clf_rf.score(X_test, Y_test))