Worked on the rapidsai/cuml repository to enhance reliability in multiclass classification workflows by implementing robust input validation. Focused on strengthening data integrity, the contribution introduced explicit dtype checks for fit, predict, and decision_function methods, ensuring that only correctly typed data is processed. This approach clarified the boundary between wrapper and estimator validation, reducing the risk of mis-typed inputs and downstream errors. Using Python and leveraging data validation techniques within a machine learning context, the work improved the predictability and robustness of multiclass model deployments, streamlining error handling and supporting more efficient debugging for data scientists in production environments.