F1 Value

By Jon

F1 Value In Classification We’re getting somewhere with our modelling evaluation! We’ve covered a variety of helpful techniques, that are all part of the toolkit when we want to assess our machine learning model fit…

ROC & AUC

By Jon

Area Under the Curve We’ve looked at precision and recall, and we’ve learnt how these simple calculations can give us a much better understanding of where our model is performing, not just how well it’s…

Precision vs Recall

By Jon

How do you want your model to perform?   Precision and Recall are very useful in understanding how your model is performing, and what situations it might be better to perform in. We’ll go into…

Confusion Matrix

By Jon

Confusion Matrix – Simple, Effective Although it’s named the ‘Confusion’ matrix, this simple table is remarkably easy to understand, especially for binary classification problems. Things can become a bit trickier when we extend the number…

Log Loss

By Jon

Log Loss: Penalising False Classifications As we have seen, pure accuracy of our machine learning model is an evaluation metric that leaves a lot to be desired. Now we move on to a slightly more…

Accuracy

By Jon

Model Accuracy Welcome to our first model evaluations post of the series! We’ll be starting with the most simple, perhaps most obvious technique; accuracy. Model accuracy can be informally defined as the proportion of predictions…