Identifiability refers to the ability to determine the unique characteristics or parameters of a model or system based on observed data. In statistics and machine learning, a model is considered identifiable if different parameter values lead to different predictions or outcomes. This ensures that the model can be accurately estimated and interpreted. In the context of econometrics or biostatistics, identifiability is crucial for drawing valid conclusions from data. If a model is not identifiable, it may produce multiple sets of parameters that fit the data equally well, making it difficult to ascertain which parameters are truly representative of the underlying process.