Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

ABSTRACT:

Premise

Advancements in machine learning and the rise of accessible "big data" provide an important opportunity to improve trait-based plant identification. Here, we applied decision-tree induction to a subset of data from the TRY plant trait database to (1) assess the potential of decision trees for plant identification and (2) determine informative traits for distinguishing taxa.

Methods

Decision trees were induced using 16 vegetative and floral traits (689 species, 20 genera). We assessed how well the algorithm classified species from test data and pinpointed those traits that were important for identification across diverse taxa.

Results

The unpruned tree correctly placed 98% of the species in our data set into genera, indicating its promise for distinguishing amo

SUBMITTER: Almeida BK 

PROVIDER: S-EPMC7394705 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature