Most multiclass classification neural networks are trained using the cross-entropy loss function:
However, it is not entirely suitable in certain situations.
I am talking about rank-consistent classifiers today, which we discussed in detail here: You Are Probably Building Inconsistent Classification Models Without Even Realizing.
Let’s understand the rationale.
In many real-world classification tasks, the class labels often possess a relative ordering between them (also called ordinal datasets):
Age group detection (child, teenager, young adult, middle-aged, and senior) is one such example, where labels possess an ordered relationship.
However, cross-entropy entirely ignores this notion.
Consequently, the model struggles to differentiate between adjacent labels, leading to suboptimal performance and classifier ranking inconsistencies.
By “ranking inconsistencies,” we mean those situations where the predicted probabilities assigned to adjacent labels do not align with their natural ordering.
For example, if the model predicts a lower probability for the child
age group than for the teenager
age group, despite the fact that teenager
logically follows child
in the age hierarchy, this would constitute a ranking inconsistency.
We could also interpret it in this way that, say, the true label for an input sample is young adult
. Then, in that case, we would want our classifier to highlight that the input sample is “at least a child,” “at least a teenager,” and “at least a young adult.”
However, since cross-entropy loss treats each age group as a separate category with no inherent order, the model struggles to learn and generalize the correct progression of age.
Ordinal datasets are quite prevalent in the industry, and typical classification models almost always produce suboptimal results in such cases.
With special attention and techniques, however, one can not only add more interpretability to ML models but also produce more accurate machine learning models.
We covered them in detail here: You Are Probably Building Inconsistent Classification Models Without Even Realizing.
While cross-entropy is undoubtedly one of the most used loss functions for training multiclass classification models, it is not entirely suitable in certain situations.
Yet, many people stick to using cross-entropy, no matter what type of dataset they are dealing with.
Learning about the advanced framework will help you make informed decisions in model building. We also implement the discussed ideas for a thorough understanding.
Read it here: You Are Probably Building Inconsistent Classification Models Without Even Realizing.
I am sure you will learn something valuable today.
👉 Over to you: What are some other challenges with nominal classification models?
For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1
Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.
Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1
Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.
Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)
Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.
Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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