Developed a performance-focused feature enhancement for the mne-tools/mne-python repository, generalizing the GeneralizingEstimator to support batch processing and vectorized scoring for classifiers. This work involved extending the estimator’s architecture to enable parallelizable scoring across batches, which improved the efficiency and scalability of model evaluation workflows. Leveraging Python, data analysis, and machine learning expertise, the implementation utilized scikit-learn to streamline classifier evaluation pipelines. By introducing batch processing and vectorized operations, the update addressed the need for faster and more scalable model assessment, allowing users to handle larger datasets and more complex workflows within the MNE ecosystem without compromising performance.