MLFlow server tracking is utilized to measure different data and statistics to analyze how generative AI is performing. Here’re a few things currently being used.
Statistics currently measured:
Sure! Here are simple definitions for F1 Score, Precision, and Recall:
Precision:
Definition: Precision is the ratio of correctly predicted positive observations to the total predicted positives. It tells us how many of the predicted positive cases were actually correct.
Formula:
Example: If a model predicts 10 positive cases and 7 of them are actually positive, the precision is
Recall (also known as Sensitivity or True Positive Rate):
Definition: Recall is the ratio of correctly predicted positive observations to all the observations in the actual class. It measures how well the model can identify all relevant cases.
Formula:
Example: If there are 20 actual positive cases and the model correctly identifies 15 of them, the recall is
F1 Score:
Definition: The F1 Score is the harmonic mean of precision and recall. It provides a single metric that balances both the precision and recall of the model.
Formula:
These metrics are often used to evaluate the performance of classification models, especially in cases where the classes are imbalanced.