In electrophotography, color reproduction is susceptible to variations in operating conditions. Calibrations are performed to ensure consistent tone reproduction. The timing of calibration directly impacts color consistency. Calibration consumes time and toner. Frequent calibration is not desirable. It is important to determine appropriate calibration timing to maintain acceptable color consistency while minimizing consumable usage and print job interruption. This paper proposes an adaptive approach that uses a decision tree (DT) to determine calibration timing. In the approach, experiments are designed to collect tone measurements under various operating conditions. Decision trees are developed with these measurements using machine learning algorithms. The resulting DTs can be used to predict tone deviations and determine appropriate calibration action based on changes in operating conditions. Experimental results demonstrate that the proposed approach can reduce the overall calibration frequency by 30.9% while maintaining desired tone consistency.