scikit learn decision tree

# import the regressor 
from sklearn.tree import DecisionTreeRegressor  
  
# create a regressor object 
regressor = DecisionTreeRegressor(random_state = 0)  
  
# fit the regressor with X and Y data 
regressor.fit(X, y) 

# arange for creating a range of values  
# from min value of X to max value of X  
# with a difference of 0.01 between two 
# consecutive values 
X_grid = np.arange(min(X), max(X), 0.01) 
  
# reshape for reshaping the data into  
# a len(X_grid)*1 array, i.e. to make 
# a column out of the X_grid values 
X_grid = X_grid.reshape((len(X_grid), 1))  
  
# scatter plot for original data 
plt.scatter(X, y, color = 'red') 
  
# plot predicted data 
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')  
  
# specify title 
plt.title('Profit to Production Cost (Decision Tree Regression)')  
  
# specify X axis label 
plt.xlabel('Production Cost') 
  
# specify Y axis label 
plt.ylabel('Profit') 
  
# show the plot 
plt.show() 

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

>>> from sklearn import tree
>>> X = [[0, 0], [1, 1]]
>>> Y = [0, 1]
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, Y)