What is Confusion Matrix?
A table that shows how often a classification model gets things right and wrong for each category.
Why It Matters
A confusion matrix reveals exactly where your model makes mistakes, helping you improve it.
Real-World Example
Seeing that your image classifier correctly identifies dogs 95% of the time but confuses cats with dogs 10% of the time.
“Understanding terms like Confusion Matrix matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Precision and Recall
Two metrics for measuring AI accuracy. Precision measures how many selected items are relevant. Recall measures how many relevant items were selected.
F1 Score
A single number that balances precision and recall into one measure of model accuracy.
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
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Related Terms
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
Precision and Recall
Two metrics for measuring AI accuracy. Precision measures how many selected items are relevant. Recall measures how...
F1 Score
A single number that balances precision and recall into one measure of model accuracy.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.