deepforest

Deep forest, also referred to as deep forest learning, is a family of machine learning models that aim to construct deep architectures using ensembles of decision trees rather than neural networks. The approach is commonly called gcForest or deep forest and was introduced as a nonparametric alternative to deep learning for classification tasks, particularly when data are limited or features are structured.

Its core idea is a cascade of forest layers. Each layer takes the original features and the class probability estimates produced by the previous layer, then outputs refined predictions. The outputs are concatenated with the inputs and passed to the next layer, allowing the model to grow in depth automatically as long as validation performance improves. The method also employs multi-grained or multi-granularity scanning to generate diverse representations from the data.

Applications include structured data classification, text categorization, and image-related tasks where informative features can be extracted beforehand. Several open-source implementations exist, and gcForest has been used as a reference framework for deep forest models. The approach can offer greater interpretability and data efficiency compared with some deep neural networks, but it may be computationally intensive and require careful tuning for large-scale problems.

Deep forest is distinct from mainstream deep learning with neural networks yet shares the goal of learning hierarchical representations. It remains a topic of research and practical interest, particularly as a non-neural alternative for scenarios with modest data sizes or where model interpretability is valued.