For those implementing vision and analysis systems in industrial plants, understanding what happens behind an algorithm like Random Forest is essential. Not only to trust its results but also to know how to integrate it with image data, what performance to expect, and its limitations. This week, we clearly explain how Random Forest is trained, how trees are built, and how it is used to classify data in real time.
Random Forest training is a supervised process starting from a labeled dataset with representative examples. To build each tree, a bootstrap sample is generated — a random sample with replacement from the original dataset. This creates diversity among trees since each one sees slightly different data.
At each tree node, the algorithm randomly selects a subset of variables (features) and evaluates all possible splits for those variables, choosing the split that best separates classes according to the Gini index, a measure of impurity. This process repeats recursively until stopping criteria are met, like maximum depth or minimum samples per node.
Each tree is built once and is not iterative like k-means. However, the full forest (which can be hundreds of trees) is trained in parallel, speeding up the process.
Once trained, trees are used for fast inference. Each new data point is passed down each tree following the pre-defined splits until reaching a leaf with a prediction. Then, predictions from all trees are combined to decide the final classification.
In plants, understanding that training is not iterative nor instantaneous helps plan resources and timing. Also, knowing classification is fast and rule-based facilitates integration with vision and control systems.
For implementers, preparing a representative labeled dataset with features extracted from images or sensors is key to training the model.
Training can be done offline, leveraging parallelism to accelerate, and the resulting model exported for plant use.
At inference, Random Forest classifies new data in milliseconds, enabling AI Edge or embedded system deployment without powerful hardware.
This architecture also helps identify important variables and eases integration with vision pipelines that extract features before classification.
Finally, knowing what Random Forest can and cannot do helps choose it for suitable tasks, avoiding attempts to use it directly on raw images or for problems requiring complex segmentation.