Model Confidence Scores

Many ML classification models can provide a confidence score which tells the user how confident the model is that it has made the correct choice.

The values of these confidence scores and what constitutes a "good" or "bad" score can vary a lot depending on the type and behaviour of the model. We often get asked why a particular model only ever seems to be 20% confident when a different model gives 99% confidence.  Here's why that happens.

Confidence in Random Forest Models

Random Forest Models are made up of an ensemble of decision tree models which are trained based on randomly selected sub-samples of the full training set - allowing the trees to learn different feature priorities based on the variance in the data that they are "assigned".

A single decision tree model cannot tell you how confident it is - the data is passed in and the algorithm traverses the branches in the tree until it reaches a decision. Within the random forest a single