Making the Most of Predictive Models

The document discusses various methods for interpreting predictive models, including linear regression, random forests, and neural networks. Linear regression interpretations can analyze coefficient magnitudes and signs. Partial least squares (PLS) decomposition can validate linear models and rank descriptors by their weights. Random forests rank descriptor importance. Neural networks can be interpreted by examining their effective weight matrices. The level of interpretation possible depends on the model and complexity of the problem being addressed.