Results for "false inference"

Failure to detect present disease.

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

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.

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

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

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

Of true negatives, the fraction correctly identified.

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

Model execution path in production.

Cost to run models in production.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.

Low-latency prediction per request.

Running predictions on large datasets periodically.

Differences between training and inference conditions.

Model relies on irrelevant signals.

Acting to minimize surprise or free energy.

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

Automated detection/prevention of disallowed outputs (toxicity, self-harm, illegal instruction, etc.).

Ability to correctly detect disease.

Identifying suspicious transactions.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Running models locally.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Time from request to response; critical for real-time inference and UX.

Reducing numeric precision of weights/activations to speed inference and reduce memory with acceptable accuracy loss.

Updating beliefs about parameters using observed evidence and prior distributions.

Prevents attention to future tokens during training/inference.

Autoencoder using probabilistic latent variables and KL regularization.

Variable enabling causal inference despite confounding.

Limiting inference usage.