A method for pre-calibrating an eye tracking apparatus of a head-mounted computing device, the method comprising: generating a generic training data set that comprises a first image of a trainer's eye and a first image label that comprises a first designated location on a generic calibration image displayed on a first graphical user interface output for display by a data-collection head-mounted computing device being worn by the trainer, and wherein the first image is captured by a first image sensor on the data-collection head-mounted computing device at a moment in time when the trainer is gazing at the first designated location; generating a trained machine learning model using the generic training data set as a training input by using a regression process whereby the trained machine learning model is able to output a gaze location given an unlabeled eye image; saving the trained machine learning model on the head-mounted computing device; generating a user-specific training data set that comprises a second image of a user's eye and a second image label that comprises a second designated location on a user-specific calibration image displayed on a second graphical user interface output for display by the head-mounted computing device being worn by the user, and wherein the second image is captured when the user is gazing at the second designated location on the user-specific calibration image, wherein the user-specific calibration image has less designated locations than the generic calibration image; generating a user-specific correction factor for the trained machine learning model using the user-specific training data set; capturing an image of the user's eye by an image sensor on the head-mounted computing device; and determining, by the trained machine learning model and using the user-specific correction factor, a first gaze direction for the user's eye using the image of the user's eye as input, characterized in that the method further comprises determining a second gaze direction for the user's eyes using an eye-feature-based method that also uses the image of the user's eye as input, wherein the eye-feature-based method is a separate and distinct technology from using the trained machine learning model; in response to determining that the first gaze direction and the second gaze direction are not within a threshold tolerance, discard the first gaze direction without use by the head-mounted computing device. generating a generic training data set that comprises a first image of a trainer's eye and a first image label that comprises a first designated location on a generic calibration image displayed on a first graphical user interface output for display by a data-collection head-mounted computing device being worn by the trainer, and wherein the first image is captured by a first image sensor on the data-collection head-mounted computing device at a moment in time when the trainer is gazing at the first designated location; generating a trained machine learning model using the generic training data set as a training input by using a regression process whereby the trained machine learning model is able to output a gaze location given an unlabeled eye image; saving the trained machine learning model on the head-mounted computing device; generating a user-specific training data set that comprises a second image of a user's eye and a second image label that comprises a second designated location on a user-specific calibration image displayed on a second graphical user interface output for display by the head-mounted computing device being worn by the user, and wherein the second image is captured when the user is gazing at the second designated location on the user-specific calibration image, wherein the user-specific calibration image has less designated locations than the generic calibration image; generating a user-specific correction factor for the trained machine learning model using the user-specific training data set; capturing an image of the user's eye by an image sensor on the head-mounted computing device; and determining, by the trained machine learning model and using the user-specific correction factor, a first gaze direction for the user's eye using the image of the user's eye as input, characterized in that the method further comprises determining a second gaze direction for the user's eyes using an eye-feature-based method that also uses the image of the user's eye as input, wherein the eye-feature-based method is a separate and distinct technology from using the trained machine learning model; in response to determining that the first gaze direction and the second gaze direction are not within a threshold tolerance, discard the first gaze direction without use by the head-mounted computing device.