Decision Tree Regression using sklearn

Decision Tree is a decision-making aproach that uses a flowchart-like tree structure 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 represents as:

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:

Step-by-Step implementation –

Step 1: Import the required libraries.

Step 2: Initialize and print the Dataset.

Step 3: Select all the rows and column 1 from dataset to “X”.

Step 4: Select all of the rows and column 2 from dataset to “y”.

Step 5: Fit decision tree regressor to the dataset

Step 6: Predicting a new value

Step 7: Visualising the result

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.

Output (Decision Tree):

https://github.com/InternityFoundation/ML_Suraj_Durgesht/blob/master/Decision_Tree.py