Diagnostics on English AI Terms Dictionary

Underfitting

Sat, 18 Jul 2026 10:19:06 +0000

Definition

Underfitting occurs when a statistical model or machine learning algorithm cannot approximate the function mapping inputs to outputs accurately. This usually happens when the model is too simple for the complexity of the data, such as using linear regression on non-linear data. It results in poor performance on both training and test datasets. To resolve underfitting, practitioners may increase model complexity, add more relevant features, reduce regularization, or train the model for more epochs until it learns the patterns effectively.

Learning curve

Sat, 18 Jul 2026 10:04:43 +0000

Definition

Typically, a learning curve displays training and validation scores on the y-axis against the number of training samples or iterations on the x-axis. It helps diagnose whether a model suffers from high bias (underfitting) or high variance (overfitting). By observing the gap between training and validation curves, practitioners can decide whether to collect more data, simplify the model, or adjust regularization parameters to improve generalization.

Summary

A learning curve plots a model’s performance metric against the amount of training data or training epochs to visualize the learning progress.

Autognostics

Sat, 18 Jul 2026 09:47:03 +0000

Definition

Autognostics refers to the self-monitoring and self-repair mechanisms embedded within intelligent systems. It allows AI agents to detect anomalies, diagnose root causes of failures, and potentially correct themselves. This concept is vital for developing robust, autonomous systems that can operate reliably in dynamic environments. By continuously assessing their own health and accuracy, these systems reduce downtime and maintenance costs while enhancing overall operational resilience.

Summary

The capability of an AI system to self-diagnose its internal state, performance issues, or errors without human intervention.