Machine Learning Project 6: Predict Salary using Random Forest Regression
Today I will be writing about Random Forest Regression Model. Random Forest is a version of Ensemble Learning. Ensemble learning in simple terms is when you take an algorithm or multiple algorithms and apply it multiple times to make it more powerful than the original version.
Unlike Decision Tree model where we built a Decision Tree to predict the value for a new data point — In Random Forest we build many Decision Trees — (typical default is 500 trees).
So instead of getting 1 prediction, in Random Forest we get many predictions for y (say 500 trees give out 500 predictions). We then take the average of all the predictions to assign that to y.
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- I have solved the same problem in project 3 using Polynomial Regression — You can check it out here.
- We then solved it Support Vector Regression — You can check that project here.
- And in the last project, we used Decision Tree Regression — It’s available here.
Today, we will use Random Forest model and see how good our prediction is.
Dataset
First let’s look at the dataset. It is Position_Salaries.csv and can be found here.
It has 3…
