In predictive modeling, unbiased models can have higher variance, & thus be less accurate. Modelers may prefer some bias to maximize accuracy. Use this tag also for questions about the bias-variance decomposition.
The bias-variance tradeoff is a fundamental issue in predictive modeling. Estimators / fitting algorithms for models that are unbiased (i.e., that have sampling distributions that are asymptotically centered on the true values) can have higher variance (i.e., be further from the true value in any given instance), and thus be less accurate. Modelers often prefer models that are somewhat biased so as to maximize accuracy.
