new_contents: # -*- coding: utf-8 -*-
  """Decision Tree regression for machine learning.
  
  Decision tree builds regression or classification models in the form of a tree
  structure. It brakes down a dataset into smaller and smaller subsets while at
  the same time an associated decision tree is incrementally developed. The final
  result is a tree with decision nodes and leaf nodes.
  
  Example:
  
          $ python regularDecisionTreeRegression.py
  
  Todo:
      *
  """
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  from sklearn.tree import DecisionTreeRegressor
  # from sklearn.preprocessing import StandardScaler
  # from sklearn.model_selection import train_test_split
  
  
  # Importing the dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  features = dataset.iloc[:, 1:2].values
  labels = dataset.iloc[:, 2].values
  
  # Splitting the Dataset into a Training set and a Test set
  """feature_train, feature_test, label_train, label_test = train_test_split(
      features, labels, test_size=0.2)"""
  
  # Feature scaling, normalize scale is important. Especially on algorithms
  # involving euclidian distance. Two main feature scaling formulas are:
  # Standardisation: x_stand = (x-mean(x))/(standard_deviation(x))
  # Normalisation: x_norm = (x-min(x))/(max(x)-min(x))
  """sc_feature = StandardScaler()
  feature_train = sc_feature.fit_transform(feature_train)
  feature_test = sc_feature.transform(feature_test)
  sc_labels = StandardScaler()
  labels_train = sc_labels.fit_transform(labels_train)
  labels_test = sc_labels.transform(labels_test)
  """
  
  # Fit the Decision Tree Regression to the dataset
  regressor = DecisionTreeRegressor(random_state=0)  # random_state for debug
  regressor.fit(features, labels)
  
  # Predict new result with the Decision Tree Regression
  y_pred = regressor.predict(6.5)
  
  # Visualising the regression results with higher resolution
  x_grid = np.arange(min(features), max(features), 0.1)
  x_grid = x_grid.reshape((len(x_grid), 1))
  plt.scatter(features, labels, color='r')
  plt.plot(x_grid, regressor.predict(x_grid), color='b')
  plt.title('Truth or Bluff (Decision Tree Regression)')
  plt.xlabel('Position level')
  plt.ylabel('Salary')
  plt.show()
  
subject: Add Decision Tree Regression in Python