Say it like this
- Run model evaluation on the test set
- Model evaluation metrics show promising results
- We need stronger model evaluation before production
Use it at work
ML work
- Model evaluation shows 92% accuracy on the test set.
- Let's add a fairness check to the model evaluation.
Before release
- We need model evaluation on real user data before launch.
- Who's reviewing the model evaluation?
Reporting
- The new model scores better in model evaluation.
- Model evaluation found problems with short questions.
Don't say this
Model evaluation happens on test data after the model has been trained, not during training itself.
Practice
Try it
4 quick exercises
1. Your colleague asks about the next step after training. What do you say?
2. The _______ shows that our model achieves 92 percent accuracy on unseen data.
3. Which sentence uses model evaluation incorrectly?
4. Rewrite in tech English: The process of checking how good our machine learning model is on new data that it has never seen before.
Say it out loud
Phrase 1 of 3
"Run model evaluation on the test set"
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More about model evaluation
Real examples(3)
- in a standup
"Yesterday I completed the model evaluation and found that our precision is 87 percent, but recall needs improvement."
- in a code review
"Please include model evaluation results in your pull request so we can verify the changes don't degrade performance."
- in a team meeting
"The model evaluation revealed overfitting, so we should regularize the model and test again."
More mistakes(2)
Handle with care. Often confused with model testing or model training. Model evaluation specifically refers to measuring performance on test data using metrics, not the training process itself.
- ✕Model evaluation is the same as model testing.✓Model evaluation measures performance with metrics; model testing checks if code runs without errors.
Testing verifies functionality, while evaluation quantifies performance against specific metrics.
- ✕We skipped model evaluation to ship faster.✓We completed model evaluation before deployment to ensure quality.
Skipping evaluation risks deploying a poor-performing model; evaluation is essential before production.
Other forms(3)
- model evaluation noun
"Model evaluation is a mandatory step before any deployment."
- evaluate the model verb
"We need to evaluate the model on unseen data."
- model evaluation metrics adjective
"The model evaluation metrics show promising results."
Often used with(5)
- run model evaluation · Most common verb phrase; means to perform the evaluation process
- model evaluation metrics · The measurements used to assess performance, such as accuracy and precision
- model evaluation results · The outcomes and findings from the evaluation process
- model evaluation framework · The structure or methodology used to conduct evaluation
- comprehensive model evaluation · A thorough evaluation covering multiple metrics and test scenarios
Similar words(3)
- model validation · Often used interchangeably, though validation typically refers to tuning, while evaluation measures final performance
- performance assessment · More general term; model evaluation is specific to machine learning models
- model benchmarking · Comparing model performance against standards or competing models
Opposites(2)
- model training
- model deployment
Questions(5)
- What does model evaluation mean in machine learning?
- Model evaluation is the process of measuring how well a trained machine learning model performs on new, unseen data. It uses specific metrics like accuracy, precision, recall, and F1 score to determine if the model is reliable and ready for real-world deployment. This step comes after training and before putting the model into production.
- How do you use model evaluation in a sentence at work?
- You might say: We completed the model evaluation and the results show 89 percent accuracy. Or: The model evaluation metrics indicate overfitting, so we need to adjust the regularization parameter. Or: Before deploying to production, we ran model evaluation on the full test set.
- Is model evaluation formal or informal?
- Model evaluation is neutral and technical. It is used in both formal contexts like technical reports and presentations, and informal contexts like team discussions and Slack messages. It is appropriate in all professional tech settings.
- What is the difference between model evaluation and model training?
- Model training is the process of teaching the model using training data. Model evaluation is the process of measuring how well the trained model performs on test data. Training happens during development; evaluation happens after training to check if the model is good enough.
- What metrics are used in model evaluation?
- Common model evaluation metrics include accuracy (percentage of correct predictions), precision (correct positive predictions out of all positive predictions), recall (correct positive predictions out of all actual positives), F1 score (balance between precision and recall), and loss functions. The choice depends on the type of problem you are solving.
