Uri Itai: Goodness of fit metrics for Multi-class Predictor

The multi-class prediction had gained popularity over recent years.
Thus measuring fit goodness becomes a cardinal question that researchers often have to deal with. Several metrics are commonly used for this task. However, when one has to decide about the right measurement, he must consider that different use-cases impose different constraints that govern this decision. A leading constraint at least in \emph{real world} multi-class problems is imbalanced data: Multi categorical problems hardly provide symmetrical data. Hence, when we observe common KPIs (key performance indicators), e.g., Precision-Sensitivity or Accuracy, one can seldom interpret the obtained numbers into the model's actual needs.

We suggest generalizing Matthew's correlation coefficient into multi-dimensions. This generalization is based on a geometrical interpretation of the generalized confusion matrix.

Uri Itai, has a phd in applied math from the Technion under the supervision of Prof. Nira Dyn. He had worked as an algorithm developer and Data Scientist in the algo trading, cancer diagnostic, automobile cyber and more currently working as a senior DS for TRST AI a startup that focus in auditing AI.

Видео Uri Itai: Goodness of fit metrics for Multi-class Predictor канала Austin Python Meetup