treeinference

Tree inference refers to the process of inferring tree-structured relationships or models from data. It encompasses reconstructing a tree’s topology and branch lengths that summarize relationships, as well as inferring the behavior of a trained tree-based model on new data. The term covers both exploratory reconstruction of relationships and the predictive use of trained trees.

In phylogenetics and related fields, tree inference aims to recover evolutionary relationships among taxa from molecular sequence data. Common approaches include distance methods such as neighbor-joining; character-based methods like maximum parsimony; and probabilistic methods including maximum likelihood and Bayesian inference, which rely on explicit models of sequence evolution. Confidence in inferred trees is assessed with bootstrap resampling or posterior probabilities, and model choice influences the inferred topology and branch lengths.

In machine learning, inference on trees usually means applying a trained decision tree or ensemble of trees (such as random forests or gradient boosted trees) to make predictions on new instances. This inference step traverses the tree according to feature values to reach a leaf with a predicted class or value. Probabilistic outputs can be calibrated to represent uncertainties, and ensembles often improve accuracy and robustness.

Other uses include inference of transmission or genealogical trees in epidemiology and genomics, where tree structure encodes relationships among cases or genomes. In these contexts, inference methods may use Bayesian or Markov chain Monte Carlo techniques to estimate ancestral relationships or transmission pathways, often under uncertainty.

Key challenges in tree inference include combinatorial complexity, model specification, data quality, and uncertainty quantification. Evaluation typically uses likelihood-based criteria, information criteria, bootstrapping, or posterior summaries, with results reported alongside confidence measures and sensitivity analyses.