X-Transform refers to a mathematical transformation applied to input features in a dataset to enhance their representation for machine learning algorithms. This technique can include various transformations, such as logarithmic scaling, normalization, or polynomial expansion, aimed at making the features more suitable for model training. By applying X-Transform, practitioners can improve the model's ability to learn complex patterns and relationships within the data. This approach is particularly valuable in situations where the original feature distributions may hinder the model's learning capacity, ultimately leading to better predictive performance.