This is the full API documentation of the DESlib. Currently the library is divided into four modules:
Dynamic Classifier Selection (DCS)¶
This module contains the implementation of techniques in which only the base classifier that attained the highest competence level is selected for the classification of the query.
deslib.dcs provides a set of key dynamic classifier selection
Dynamic Ensemble Selection (DES)¶
Dynamic ensemble selection strategies refer to techniques that select an ensemble of classifier rather than a single one. All base classifiers that attain a minimum competence level are selected to compose the ensemble of classifiers.
deslib.des provides a set of key dynamic ensemble selection
- DES base class
- DES Clustering
- Dynamic Ensemble Selection performance (DES-P)
- k-Nearest Output Profiles (KNOP)
- k-Nearest Oracle-Eliminate (KNORA-E)
- k-Nearest Oracle Union (KNORA-U)
- DES Multiclass Imbalance (DES-MI)
This module provides the implementation of static ensemble techniques that are usually used as a baseline for the comparison of DS methods: Single Best (SB), Static Selection (SS), Stacked classifier and Oracle.
deslib.static provides a set of static ensemble methods which are
often used as a baseline to compare the performance of dynamic selection
Utility functions for ensemble methods such as diversity and aggregation methods.
deslib.util This module includes various utilities. They are divided
into four parts:
deslib.util.aggregation - Implementation of aggregation functions such as majority voting and averaging. Such functions can be applied to any list of classifiers.
deslib.util.diversity - Implementation of different measures of diversity between classifiers.
deslib.util.prob_functions - Functions to estimate the competence of a base classifier based on the probability estimates.
deslib.util.instance_hardness - Functions to measure the hardness level of a given instance
deslib.util.faiss_knn_wrapper - Wrapper for Facebook AI fast similarity search on GPU
deslib.util.datasets - Provides methods to generate synthetic data.