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:
- Conditions [Decision Nodes]
- 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.
# 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
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- Step 2: Initialize and print the Dataset.
# 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)
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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”.
# select all rows by : and column 1 # by 1:2 representing features X = dataset[:, 1 : 2 ].astype( int )
# print X print (X)
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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”.
# select all rows by : and column 2 # by 2 to Y representing labels y = dataset[:, 2 ].astype( int )
# print y print (y)
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Output:
[ 1000 3000 5000 8000 6500 7000 15000 20000 21000 25000 27000 30000 52000 80000]
- Step 5: Fit decision tree regressor to the dataset
# 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
# 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)
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Output:
Predicted price: 8000
- Step 7: Visualising the result
# 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.
# 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' ])
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Output (Decision Tree):