Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization

1. 1Decouple feature learning from data-generating-process (DGP) identifiability to understand when neural networks fail to learn OOD-relevant representations
2. 2Analyze the in-distribution (ID) training window to identify potential biases in the data
3. 3Apply feature engineering techniques to introduce identifiability bias and improve OOD generalization
4. 4Test the neural network on out-of-distribution data to evaluate its extrapolation capabilities
5. 5Compare the performance of the neural network with and without feature engineering to measure the impact on OOD generalization

Machine learning engineers and researchers working on deep neural networks can benefit from understanding the limitations of their models in extrapolating to out-of-distribution data and how to improve this using feature engineering

💡 Feature engineering can introduce identifiability bias, which can improve the ability of neural networks to generalize to out-of-distribution data

🤖 Does your neural network extrapolate? Learn how feature engineering can help with out-of-distribution generalization #ML #OODGeneralization