Random Forest Regression is a supervised learning technique that does regression using the ensemble learning method. The ensemble learning approach combines predictions from numerous machine learning algorithms to get a more accurate forecast than a single model.

The above graphic depicts the structure of a Random Forest. You'll note that the trees are running in parallel with no interaction between them. During training, a Random Forest constructs many decision trees and outputs the mean of the classes as the forecast of all the trees. Let's go through the steps to acquire a better knowledge of the Random Forest algorithm: 1. Choose k data points at random from the training set. 2. Create a decision tree based on these k data points. 3. Repeat steps 1 and 2 for the number N of trees you wish to create.

Make each of your N-tree trees anticipate the value of y for the new data point in question, and then assign the new data point to the average of all of the predicted y values. A Random Forest Regression model is effective and precise. It often outperforms on a wide range of problems, including those with non-linear connections. The disadvantages are as follows: there is no interpretability, overfitting is possible, and we must pick the amount of trees to include in the model.


  • from sklearn.ensemble import RandomForestRegressor

Full Code Of Implementing Random Forest Regression

It's time to put our coding hats on! In this part, we'll look at how to apply Support Vector Regression with a dataset (Download the dataset here). In this case, we must forecast an employee's wage based on a few independent variables. This is a standard HR analytics project!