How is Data Quality Ensured for Machine Learning Models?
Data quality is crucial for the success of machine learning models in catalysis. Ensuring high-quality data involves several steps:
1. Data Preprocessing: Cleaning and normalizing data to remove noise and inconsistencies. 2. Feature Engineering: Identifying and creating relevant features that capture the essential characteristics of catalysts. 3. Data Augmentation: Generating additional data through simulations or by combining existing datasets to improve model robustness. 4. Validation and Testing: Splitting data into training, validation, and test sets to evaluate model performance and avoid overfitting.
