A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.
- Pick at random K data points from the training set.
- Build the decision tree associated with those K data points.
- Choose the number Ntree of trees you want to build and repeat step 1 & 2.
- For a new data point, make each one of your Ntree trees predict the value of Y for the data point, and assign the new data point the average across all of the predicted Y values.
Below is the step by step Python implementation.
Step 1 : Import the required libraries.
Step 2 : Import and print the dataset
Step 3 : Select all rows and column 1 from dataset to x and all rows and column 2 as y
Step 4 : Fit Random forest regressor to the dataset
Step 5 : Predicting a new result
Step 6 : Visualising the result
- ML | Logistic Regression using Python
- Python | Implementation of Polynomial Regression
- Linear Regression (Python Implementation)
- ML | Multiple Linear Regression using Python
- Python | Linear Regression using sklearn
- Univariate Linear Regression in Python
- Python | Decision Tree Regression using sklearn
- Random Numbers in Python
- Generating random Id's in Python
- random.seed( ) in Python
- Random Walk (Implementation in Python)
- numpy.random.rand() in Python
- Python | Select random value from a list
- numpy.random.randn() in Python
- Generating Random id's using UUID in Python
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.