Classifying High-Dimensional Phenotypes with Ensemble Learning
Global variables.
Detect anomalies.
Parameters for resampling and training a dataset.
Classify phenotypes via ensemble learning.
Equate factors levels.
Evaluate a phenotype classification model.
Compute interquartile range.
Find outlier indices.
Preprocessing for phenotype classification via ensemble learning.
Generate predictions for phenotype ensemble.
Pipe.
A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification.