Data Science in R: From Basics to Machine Learning/

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Random Forest

Random Forest

Learn to construct and use random forest models using tidymodels.

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Random forest models are a popular machine learning algorithm in data science. Random forests can be used for both regression and classification tasks and are known for high accuracy and their ability to handle large datasets. In this lesson, we delve into the steps involved in creating a random forest model using tidymodels.

At a very high level, a random forest model uses many decision trees to make predictions. Each decision tree is built using a different subset of the training data, and the final prediction is made by taking the average or majority vote of the predictions from all the trees.

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Random forests are a majority vote system
Random forests are a majority vote system

It’s worth noting that random forests are just one type of decision tree ensemble method. Other ensemble methods, such as gradient boosting machines and AdaBoost, can be used for similar purposes. In addition to the random forest model set up in this lesson, tidymodels also provides functionality for implementing some of those other methods.

We won’t discuss in detail the theory behind random forest models here. Still, it’s essential to remember that random forest models are appropriate when there’s a need for high accuracy and the input dataset is large or complex. In particular, random forest models offer a fair trade-off between having a high degree of accuracy and being explainable. They aren’t as easily explainable as linear regression models, but there are techniques available to get a fairly good understanding of what drives their behavior.

Pros and cons of random forest models

Random forest models work well with datasets with many variables and when there’s a potential for nonlinear relationships between the response and predictor variables. Random forest models are also helpful when dealing with missing data or outliers, as they are robust to these issues.

In summary, when choosing to use random forest models, there are several advantages and disadvantages to consider. These primarily revolve around their unique structure based on decision trees. Their benefits include:

  • High predictive accuracy: Random forest models tend to have high ...