skcriteria.agg.codas module
Combinative Distance-Based Assessment - CODAS.
The CODAS method evaluates alternatives using two distance metrics. It first calculates both Euclidean and Taxicab distances from a negative-ideal solution, which represents the worst performance across all criteria.
The method constructs a relative assessment matrix based on these distances, where the Euclidean distance serves as the primary measure, and the Taxicab distance acts as a tiebreaker when alternatives are very similar according to the first. The final ranking is determined by summing the values in the relative assessment matrix for each alternative, with higher scores indicating better performance.
- skcriteria.agg.codas.codas(matrix, weights, tau)[source]
Execute CODAS without any validation and assuming tau value.
- class skcriteria.agg.codas.CODAS(tau=0.02)[source]
Bases:
SKCDecisionMakerABCRank alternatives using CODAS method.
COmbinative Distance-based ASsessment (CODAS) is an MCDM method that ranks alternatives by comparing how far they are from the worst possible solution (anti-ideal), simultaneously using Euclidean distance as the primary measure and Taxicab (Manhattan) distance as a tiebreaker when alternatives are very similar according to the first.
- Parameters:
tau (float, optional (default=0.02)) – tau is the threshold parameter that can be set by the decision-maker. Used to construct the relative assessment matrix.
- Raises:
ValueError: – If the objectives contain a minimize objective. If the decision matrix is not normalized.
UserWarning: – If tau is not set between 0.01 and 0.05.
References
- property tau
Which tau value will be used.