Description: The ‘Ground Truth’ refers to the accurate data used as a reference to evaluate the performance of a model in the fields of AutoML and Data Science. This data is essential for establishing a standard against which the predictions and decisions generated by a machine learning model can be measured. Ground Truth acts as a set of known labels or outcomes that allow data scientists and machine learning engineers to validate the effectiveness of their algorithms. Without a well-defined Ground Truth, it is challenging to determine whether a model is functioning correctly or if it needs adjustments. This concept is crucial in the creation of predictive models, as it provides a solid foundation for the evaluation and continuous improvement of these models. Furthermore, the quality of the Ground Truth directly influences the quality of the decisions made based on the model’s results, making it a vital component in the lifecycle of machine learning model development.
