Confusion Matrix
A confusion matrix is a table that summarizes the performance of a classification model by showing the counts of correct and incorrect predictions broken down by actual and predicted class, revealing exactly which categories the model confuses with one another.
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Glossary Terms(7)
Explainable AI (XAI)
Explainable AI (XAI) is a set of methods and practices for making the decisions and internal workings of machine learning models understandable to humans, so t…
Model Registry
A model registry is a centralized system for storing, versioning, and tracking machine learning models throughout their lifecycle, recording metadata such as t…
Confusion Matrix
A confusion matrix is a table that summarizes the performance of a classification model by showing the counts of correct and incorrect predictions broken down…
Precision and Recall
Precision and recall are two complementary metrics for evaluating a classification model: precision measures how many of the model's positive predictions were…
F1 Score
The F1 score is a single classification metric that combines precision and recall into one number by calculating their harmonic mean, providing a balanced meas…
ROC Curve
A ROC (Receiver Operating Characteristic) curve is a graph that plots a binary classification model's true positive rate against its false positive rate across…
Cross-Validation
Cross-validation is a model evaluation technique that repeatedly splits a dataset into different training and validation subsets, trains and tests the model on…