Exploring Classification Metrics Explained
Let's dive into the details surrounding Classification Metrics Explained.
- Classification
- In this video, we cover the most important evaluation
- ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information ...
- Subscribe to RichardOnData here: https://www.youtube.com/channel/UCKPyg5gsnt6h0aA8EBw3i6A?sub_confirmation=1 In this ...
- You may have come across the terms "Precision, Recall, and F1" when reading about
In-Depth Information on Classification Metrics Explained
This precision vs recall example In this video we will go over following concepts, What is true positive, false positive, true negative, false negative What is precision ... One of the fundamental concepts in machine learning is the Confusion There are many evaluation
In this video we refer to the evaluation
That wraps up our extensive overview of Classification Metrics Explained.
