Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

NeurIPS 2018 Mikhail BelkinDaniel HsuPartha Mitra

Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong generalization performance for "overfitted" / interpolated classifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests... (read more)

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