In this paper, we introduce a dual-focus training approach that explicitly teaches a model what it should predict and then extends this knowledge by teaching it what not to predict. Our core contribution is a custom loss function that not only penalizes deviations from the ground truth label but also imposes additional costs when the prediction drifts toward a false positive region. The proposed solution is effective for regressions and classifications of one or two types of objects and is based on adding additional surrogate classes of negative data that teaches the models to discriminate features specific to false classes from the positive ones. We evaluated the method on multiple types of problems: regression-based tasks (glint detection dataset) and detection tasks (brain tumors, red blood cell detection, and parking slot occupancy). Empirical results demonstrate substantial reductions in false negatives and false positives, highlighting the practical value of explicitly encoding negative-avoidance signals in the training objective. Results indicate significant improvements in both recall and precision, with notable IoU gains (up to 14.79%) and a 12.30% reduction in mean absolute error for the regression task.