Anomaly Detection (AD) is a popular unsupervised learning technique, but AD implementations are difficult to test, understand, and ultimately improve. Contributing factors for these difficulties include the lack of a specification to test against, output differences (on the same input) between toolkits that supposedly implement the same AD algorithm, and no linkage between learning parameters and undesirable outcomes. We have implemented DeAnomalyzer, a black-box tool that improves AD reliability by addressing two issues: nondeterminism (wide output variations across repeated runs of the same implementation on the same dataset) and inconsistency (wide output variations between toolkits on the same dataset). Specifically, DeAnomalyzer uses a feedback-directed, gradient descent-like approach to search for toolkit parameter settings that maximize determinism and consistency. DeAnomalyzer can operate in two modes: univariate, without ground truth, targeted to general users, and bivariate, with ground truth, targeted to algorithm designers and developers. We evaluated DeAnomalyzer on 54 AD datasets and the implementations of four AD algorithms in three popular ML toolkits: MATLAB, R, and Scikit-learn. The evaluation has revealed that DeAnomalyzer is effective at increasing determinism and consistency without sacrificing performance, and can even improve performance.