Results for "positive predictive value"

Of predicted positives, the fraction that are truly positive; sensitive to false positives.

Often more informative than ROC on imbalanced datasets; focuses on positive class performance.

Of true positives, the fraction correctly identified; sensitive to false negatives.

Plots true positive rate vs false positive rate across thresholds; summarizes separability.

Expected cumulative reward from a state or state-action pair.

Fundamental recursive relationship defining optimal value functions.

AI predicting crime patterns (highly controversial).

Scalar summary of ROC; measures ranking ability, not calibration.

Stores past attention states to speed up autoregressive decoding.

Combines value estimation (critic) with policy learning (actor).

Maximum expected loss under normal conditions.

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Activation max(0, x); improves gradient flow and training speed in deep nets.

Optimal control for linear systems with quadratic cost.

Learning by minimizing prediction error.

Average of squared residuals; common regression objective.

Expected return of taking action in a state.

Sample mean converges to expected value.

Optimizing policies directly via gradient ascent on expected reward.

Approximating expectations via random sampling.

Model optimizes objectives misaligned with human values.

Directly optimizing control policies.

Inferring and aligning with human preferences.

Normalized covariance.

Returns above benchmark.

Optimizes future actions using a model of dynamics.

High-fidelity virtual model of a physical system.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Feature attribution method grounded in cooperative game theory for explaining predictions in tabular settings.