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:
- 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.
- Step 2: Initialize and print the Dataset.
- Step 3: Select all the rows and column 1 from the dataset to “X”.
- Step 4: Select all of the rows and column 2 from the 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):
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