A Technique to Understand TP, TN, FP and FN

Do you struggle to classify predictions as one of TP, TN, FP, and FN?

If yes, here’s a simple technique I recommend using.

A video version of this technique is available at the top.

If you prefer reading...let’s dive in below.

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When labeling any binary classification prediction, ask two questions:

• Question 1) Did the model get it right?

  • The answer will be either Yes or No.

    • Yes means True.

    • No means False.

• Question 2) What was the predicted class?

  • The answer will be either Positive or Negative.

Next, just combine the above two answers to get the final label.

For instance, say the actual and predicted class were positive.

• Question 1) Did the model get it right?

  • Answer: Yes, which means TRUE.

• Question 2) What was the predicted class?

  • Answer: POSITIVE.

The final label: TRUE POSITIVE.

Simple, right?

The following visual summarizes this:

As an exercise, complete the table below.

Consider:

• The cat class → Positive.

• The dog class → Negative.

Let me know your answers.

👉 Over to you: Do you know any other techniques to label binary classification predictions?

Thanks for reading!

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P.S. 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.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – 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.