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, confidence is calculated by assigning each tree a "vote" on the outcome class and then working out the distributions of votes across the possible outcome classes as a percentage.

Say we train a random forest model containing 100 trees on a company sector/industry classification problem with 5 classes. In theory, some of the trees will learn to prioritise the most important features in the dataset. Likewise, we can assume that some of the trees will be trained on less representative sub-samples of the training data and will prioritise less discriminative features.

When we predict on an unseen data sample we might get an output like this:

We specialise in using AI to improve user experience for customers of high end grocery stores

35 Trees votes for "Consumer Goods"
25 Trees voted for "IT & Technology"
22 Trees voted for "Health & Beauty"
12 Trees voted for "Retail"
6 Trees voted for "Automotive Manufacturing"

I used 100 random trees in my model for easy maths, so we would say in this case that the model is 35% confident that this description is from a company in consumer goods.