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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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