What Are Some Challenges in Using Scikit-learn for Catalysis?
Despite its advantages, there are some challenges when using scikit-learn in catalysis:
Data Quality: The accuracy of machine learning models heavily depends on the quality of the data. In catalysis, experimental data may be noisy or incomplete. Domain Expertise: Interpreting the results of machine learning models requires domain expertise in catalysis to ensure the findings are scientifically valid. Computational Resources: Training complex models on large datasets can be computationally intensive, requiring significant computational resources.
