A Comparative Analysis of Gradient Boosted Machines and Random Forest

As a data scientist or data science enthusiast, you might have heard of Gradient Boosted Machines (GBMs) and Random Forests. These two methods are popular classification algorithms that can be used to predict the outcome of an event. The question is which one should you use? In this article, we will compare these two algorithms in terms of performance and effectiveness in different scenarios.

Gradient Boosted Machines (GBM) have become the most popular approach to machine learning. When used for classification tasks, GBM usually results in models with high accuracy and low bias. When compared to other tree-based ensemble approaches such as Random Forests, the computation time required for model training with GBM is dramatically low.

However, there is still a lack of public knowledge regarding the effectiveness and applicability of GBM in certain situations compared to other machine learning algorithms such as Random Forests. To understand the differences between these two algorithms, we will compare them through various parameters such as computational efficiency, accuracy, and variable importance measures.

Gradient Boosted Machine (GBM) is a powerful supervised learning algorithm that builds multiple decision trees with gradient boosting to learn the optimal discriminative model for prediction. GBM is based on an ensemble of weak learners which are typically decision trees. The main idea behind GBM is that given its non-linear nature, it is difficult for any single decision tree to fit all training examples (because of high variance), but by combining several smaller decision trees together to form a strong learner, it can fit most parts of the data well while keeping the variance low.

Random Forests (RF) is another ensemble learning technique widely used for both classification and regression tasks. It also uses multiple weak learners that are random decision trees. A random subset of features is selected at each node split and this process is repeated several times until all possible feature subsets have been explored. The final prediction is made based on aggregate weightings assigned to each tree.

Gradient Boosted Machines can be considered as an adaptive alternative to boosting. Unlike traditional bagging which works by reducing class weights in each iteration of the bootstrap sample, GBM uses a tree-dependent stopping criterion.

The Random Forest approach delivers high accuracy and low bias on diverse datasets as shown by several empirical studies summarised in. This makes the algorithm a better option for structured and imbalanced datasets.

We can say that GBM tends to be more powerful than RF in terms of accuracy but it also requires more computation time to grow the final model. As mentioned above, there is no need for pruning in RF so its computational requirements are relatively low compared to GBMs. However, Random Forests can handle extremely high dimensional input sets which makes them an excellent choice when dealing with a large number of variables (e.g., genomics data) whereas GBMs cannot effectively work with such high-dimensional data due to overfitting risk (especially when dealing with a large number of variables).

In conclusion, if one is working on a dataset that has many variable features and the prediction accuracy is not as important (at the expense of computation time) then RF can be considered. On the other hand, GBMs are better suited for datasets that have very few or fewer input features and where high accuracy predictions are required. However, there are instances when either GBM or RF can perform equally well depending on how they are tuned such as in.

Originally published at https://protonautoml.com on July 23, 2021.