import numpy as np
import pandas as pd
from sklearn import tree
features = [[140, 1], [130, 1], [135, 1], [145, 1], [150, 2], [160, 2], [170, 2], [165, 2]]
labels = [1, 1, 1, 1, 2, 2, 2, 2]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(features, labels)
clf.predict([[150, 2]])
from sklearn.metrics import accuracy_score
y_pred=[1,1,2,1,2,1,2,1,1,1,2]
y_true=[1,1,2,1,2,1,2,1,1,2,1]
accuracy_score(y_pred,y_true)
Result: 0.8181818181818182
