decision tree regression

• 0. Data Preprocessing
  • 0.1 Importing the libraries
  • 0.2 Importing the dataset
  • 0.3 Split into X & y
• 1. Training the decision tree regression model on the whole dataset
• 2. Predicting a new result with Linear Regression
• 3. Visualising the Decision Tree Regression results (higher resolution)

0. Data Preprocessing

0.1 Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

0.2 Importing the dataset

dataset = pd.read_csv('Position_Salaries.csv')
dataset

	Position	Level	Salary
0	Business Analyst	1	45000
1	Junior Consultant	2	50000
2	Senior Consultant	3	60000
3	Manager	4	80000
4	Country Manager	5	110000
5	Region Manager	6	150000
6	Partner	7	200000
7	Senior Partner	8	300000
8	C-level	9	500000
9	CEO	10	1000000

0.3 Split into X & y

X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

1. Training the decision tree regression model on the whole dataset

from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor()
regressor.fit(X, y)

DecisionTreeRegressor()

2. Predicting a new result with Linear Regression

regressor.predict([[6.5]])

array([150000.])

3. Visualising the Decision Tree Regression results (higher resolution)

X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()