# RANDOM FOREST REGRESSIONS

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.

# THE MAIN CODE OF RANDOM FOREST REGRESSION

# 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!

`from sklearn.ensemble import RandomForestRegressor`