Hierarchical Performance Metrics and Where to Find Them

What metrics you should use to measure the performance of your hierarchical classification model. Hierarchical machine learning models are one top-notch trick. As discussed in previous posts, considering the natural taxonomy of the data when designing our models can be well worth our while. Instead of flattening out and ignoring those inner hierarchies, we’re able to use them, making our models smarter and more accurate.

Performance Metrics for Machine Learning Models

There are various metrics that we can use to evaluate the performance of ML algorithms, classification as well as regression algorithms. We must carefully choose the metrics for evaluating ML performance because,

Understanding Performance metrics for Machine Learning Algorithms

Understanding Performance metrics for Machine Learning Algorithms Performance metrics explained — How do they work and when to use which?

Performance Metrics for Classification Machine Learning Problems

Accuracy, Precision, Recall, F1 Score, ROC AUC, Log loss. Many learning algorithms have been proposed. It is often valuable to assess the efficacy of an algorithm.