X-Shift is a technique in machine learning where input features are adjusted or shifted to improve the performance of predictive models. This method involves altering the distribution of input data to ensure that models can learn more effectively from the available features. By shifting data, practitioners can mitigate issues such as class imbalance, outliers, or noise in the dataset. This technique can be particularly useful in scenarios where certain features dominate the learning process, allowing for a more balanced and accurate representation of the underlying data.