by Entre Computer Services | Sep 25, 2025
Underfitting occurs when a model is too simple to capture patterns in the data, leading to poor accuracy both during training and in real use. Why it matters: Produces weak models that miss important insights. Often caused by not enough training data or overly... by Entre Computer Services | Sep 25, 2025
Overfitting happens when a model learns the training data too well, including noise and irrelevant details, so it performs poorly on new data. Why it matters: Produces AI that looks accurate in testing but fails in real-world use. A common mistake in machine learning.... by Entre Computer Services | Sep 25, 2025
Black box AI describes systems (often deep learning models) where the internal decision-making process is too complex for humans to interpret. Why it matters: Powerful but opaque — users can’t easily tell how outputs are generated. Creates challenges for compliance,... by Entre Computer Services | Sep 25, 2025
A confidence score is the AI system’s estimate of how certain it is about its prediction or output, usually expressed as a percentage or probability. Why it matters: Helps users judge whether to trust an AI’s response. Useful in areas like medical imaging (e.g., “90%... by Entre Computer Services | Sep 25, 2025
Model drift happens when an AI model’s performance degrades over time because the data it sees in the real world differs from the data it was trained on. Why it matters: Leads to less accurate predictions and potentially harmful decisions. Common in environments that...