PEAXACT Tour Part 7 – Performing Classification
It's now time to look into the Classification Model and how it can be used to identify categorical features from unknown samples.
Classification Model
A Classification Model uses the spectra to distinguish classes of samples. All classification methods relate back to the spectral similarity between the samples. In the training step, the model learns how similar samples inside a class are and where ambiguities between classes occur.
Training a Classification Model requires categorical features, e.g., material IDs, to form classes of samples with identical values. The pretreatment emphasizes the spectral characteristics of each class and helps to distinguish between them. In an ideal case, all given classes can be perfectly separated and identified. The Cluster Plot in the Data Inspector is a helpful preview, but the final assessment of the most suitable Classification Model is typically done with the Confusion Matrix.
Identification Analysis
When analyzing unknown samples, the Classification Model assigns them to the class with the highest similarity. In addition, the assignment of a sample to a certain class is accompanied by diagnostic values such as the Similarity and Class Probability, visible in the Report Table.
Classification Models can be deployed to field instruments for automated real-time analysis – just like any other PEAXACT model. But first, let's take a look into one last but very powerful part of the model.