Question

Create a Decision tree classifier in sci-kit learn using the Data given below,

features = [[2,100],[6,25],[1,300],[1,1000],[4,100],[10,100]]

Label = [1,2,1,1,2,2]

Note : 1 - Sports/Race Car and 2 - Family Car​

Answer 1

Answer:

from sklearn.tree import DecisionTreeClassifier

features = [[2, 100], [6, 25], [1, 300], [1, 1000], [4, 100], [10, 100]]

Label = [1, 2, 1, 1, 2, 2]

clf = DecisionTreeClassifier(random_state=0)

clf.fit(features,Label)

x1,x2 = input().split()

print(clf.predict([[x1, x2]]))

#SPJ3