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Python | Decision Tree Regression using sklearn

  • Difficulty Level : Easy
  • Last Updated : 20 Oct, 2021

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs, and utility.
Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.

The branches/edges represent the result of the node and the nodes have either: 

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  1. Conditions [Decision Nodes]
  2. Result [End Nodes]

The branches/edges represent the truth/falsity of the statement and take makes a decision based on that in the example below which shows a decision tree that evaluates the smallest of three numbers:  




Decision Tree Regression: 
Decision tree regression observes features of an object and trains a model in the structure of a tree to predict data in the future to produce meaningful continuous output. Continuous output means that the output/result is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.

Discrete output example: A weather prediction model that predicts whether or not there’ll be rain on a particular day. 
Continuous output example: A profit prediction model that states the probable profit that can be generated from the sale of a product.
Here, continuous values are predicted with the help of a decision tree regression model.

Let’s see the Step-by-Step implementation – 

  • Step 1: Import the required libraries. 

Python3




# import numpy package for arrays and stuff
import numpy as np 
  
# import matplotlib.pyplot for plotting our result
import matplotlib.pyplot as plt
  
# import pandas for importing csv files 
import pandas as pd 
  • Step 2: Initialize and print the Dataset.

Python3




# import dataset
# dataset = pd.read_csv('Data.csv') 
# alternatively open up .csv file to read data
  
dataset = np.array(
[['Asset Flip', 100, 1000],
['Text Based', 500, 3000],
['Visual Novel', 1500, 5000],
['2D Pixel Art', 3500, 8000],
['2D Vector Art', 5000, 6500],
['Strategy', 6000, 7000],
['First Person Shooter', 8000, 15000],
['Simulator', 9500, 20000],
['Racing', 12000, 21000],
['RPG', 14000, 25000],
['Sandbox', 15500, 27000],
['Open-World', 16500, 30000],
['MMOFPS', 25000, 52000],
['MMORPG', 30000, 80000]
])
  
# print the dataset
print(dataset) 

Output:

[['Asset Flip' '100' '1000']
 ['Text Based' '500' '3000']
 ['Visual Novel' '1500' '5000']
 ['2D Pixel Art' '3500' '8000']
 ['2D Vector Art' '5000' '6500']
 ['Strategy' '6000' '7000']
 ['First Person Shooter' '8000' '15000']
 ['Simulator' '9500' '20000']
 ['Racing' '12000' '21000']
 ['RPG' '14000' '25000']
 ['Sandbox' '15500' '27000']
 ['Open-World' '16500' '30000']
 ['MMOFPS' '25000' '52000']
 ['MMORPG' '30000' '80000']]
  • Step 3: Select all the rows and column 1 from the dataset to “X”.

Python3






# select all rows by : and column 1
# by 1:2 representing features
X = dataset[:, 1:2].astype(int
  
# print X
print(X)

Output:

[[  100]
 [  500]
 [ 1500]
 [ 3500]
 [ 5000]
 [ 6000]
 [ 8000]
 [ 9500]
 [12000]
 [14000]
 [15500]
 [16500]
 [25000]
 [30000]]
  • Step 4: Select all of the rows and column 2 from the dataset to “y”.

Python3




# select all rows by : and column 2
# by 2 to Y representing labels
y = dataset[:, 2].astype(int
  
# print y
print(y)

Output:

[ 1000  3000  5000  8000  6500  7000 15000 20000 21000 25000 27000 30000 52000 80000]
  • Step 5: Fit decision tree regressor to the dataset

Python3




# 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)

Output:

DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,
                      max_features=None, max_leaf_nodes=None,
                      min_impurity_decrease=0.0, min_impurity_split=None,
                      min_samples_leaf=1, min_samples_split=2,
                      min_weight_fraction_leaf=0.0, presort='deprecated',
                      random_state=0, splitter='best')
  • Step 6: Predicting a new value

Python3




# predicting a new value
  
# test the output by changing values, like 3750
y_pred = regressor.predict([[3750]])
  
# print the predicted price
print("Predicted price: % d\n"% y_pred) 

Output:

Predicted price:  8000
  • Step 7: Visualising the result

Python3




# 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()

  • Step 8: The tree is finally exported and shown in the TREE STRUCTURE below, visualized using http://www.webgraphviz.com/ by copying the data from the ‘tree.dot’ file.

Python3




# import export_graphviz
from sklearn.tree import export_graphviz 
  
# export the decision tree to a tree.dot file
# for visualizing the plot easily anywhere
export_graphviz(regressor, out_file ='tree.dot',
               feature_names =['Production Cost']) 

Output (Decision Tree): 


 




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