Random Forest Regression
Random Forests Regression is an ensemble learning method that combines multiple Decision Tree Regressions. The method uses a multitude of decision trees to train and predict values. Random Forests reduces the over-fitting in comparison to using a single Decision Tree model.
For a deeper understanding of Random Forest Regression, use the following resources:
In this practise session, we will learn to code Random Forest Regression.
We will perform the following steps to build a simple Random Forest Regressor using the Beer dataset from How To Choose The Perfect Beer Hackathon.
Step 1. Data Preprocessing
- Importing the libraries.
- Importing the data set.
- Dealing with categorical data.
- Classifying dependent and independent variables.
- Creating training and test sets.
Step 2. Decision Tree Regression
- Create a Random Forest Regression
- Training the regressor with training data.
- predicting the salary for a test set.
- Calculating score from Root Mean Log Squared Error.
Click on Start/Continue Hackathon to go to the practise page.
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