Machine learning based tone consistency calibration decisions

A method for making a decision-tree based tone consistency calibration timing decision comprises:

• measuring, with on-board sensors and in conjunction with a first tone consistency calibration, a first state of a printer, the first state comprising at least a temperature parameter and a relative humidity parameter;
• measuring a second state of the printer with the sensors;
• determining, with a computer processor, if changes between the first state and second state justify a second tone consistency calibration by applying the temperature parameter and relative humidity parameter to a root node in a pruned decision tree and moving through internal nodes to a final node of the pruned decision tree, the final node comprising a binary calibration decision;
• and
• if the binary calibration decision indicates a second tone consistency calibration should be performed, then:
• waiting until the printer completes a current print job;
• and
• performing the second tone consistency calibration, the second tone consistency calibration comprising adjusting a developer voltage level;
• and
• if the binary calibration decision output from the decision tree indicates the changes between the first state and second state do not justify a second tone consistency calibration, then re-measuring the second state of the printer at later time.