Machine Learning with Python-Python | Decision Tree Regression using sklearn

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:

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

The branches/edges represent the truth/falsity of the statement and takes 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 in 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
• 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)

  

• Step 3: Select all the rows and column 1 from 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)

  

• Step 4: Select all of the rows and column 2 from 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)

  

• 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)

  

• 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)

  

• 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):