How can we build trust in machine learning systems by ensuring that they generalize reliably to new and unseen data?

Since criteria such as AIC and Bayesian information criterion (BIC) have a tendency to overfit in model selection in high-dimensional data; EBIC (extended BIC), ... Mar 31, 2021 ... Undetected overfitting could result in incorrect predictions or categorizations. Question 6: How do financial institutions manage AI risks ... Nov 6, 2020 ... We present an information-theoretic framework for under- standing overfitting and underfitting in machine learning and prove the formal ... Feb 24, 2025 ... ⚬ Challenges such as overfitting, enhancing model accuracy, and trade-offs between ac- curacy and generalizability. • Modeling and Forecast ... Sep 10, 2017 ... Subsequently, the model is helpful about its underlying informational collection, not to some other informative groups. How do you ensure you're ... v To provide a more judicious penalty term than AIC, or AIC-type criteria to balance the overfitting and underfitting risks of a model. The Concept of ... To address this issue, prior research has adopted two approaches: contrastive learning and information maximization. Contrastive-learning [10, 19, 34, 35, 14, ... Selection for predictive patterns drives overfitting, the replication crisis, and risk seeking. This article summarizes the negative implications of these ... Feb 10, 2024 ... Overfitting occurs when a trading strategy is excessively tuned to historical data, capturing not just the underlying market signals but also the noise. Oct 15, 2010 ... A highly overfitted decision boundary might perfectly classify the training data, even if the response patterns contain no information about the ...