What method can help interpret complex machine learning models?

Machine learning models, especially complex ones like ensemble methods or deep learning architectures, can often become "black boxes," making it difficult to understand how they arrive at their predictions. Model explainability techniques are designed specifically to address this interpretability challenge. These techniques provide insights into what factors are influencing model predictions, thus enabling a clearer understanding of the model's decision-making process.

Examples of model explainability techniques include LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). These methods help by attributing the output of a model to its input features, showing which features were most influential in a specific prediction.

Other methods such as feature selection, while useful for improving model performance and reducing dimensionality, do not directly address how the model operates or makes predictions. Data normalization is primarily concerned with preprocessing data to achieve consistency and does not provide insights into model interpretation. Similarly, overfitting prevention focuses on enhancing model generalizability rather than explaining individual predictions or model behavior. Therefore, model explainability techniques stand out as the most relevant approach for interpreting complex machine learning models.