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ML Model Evaluation: 11 Metrics, Sampling Methods & Tips for Success
Machine learning (ML) model evaluation involves assessing the performance of a model using a set of metrics to understand its effectiveness and accuracy.
ML Model Evaluation: 11 Metrics, Sampling Methods & Tips for Success

What Is ML Model Evaluation?

Machine learning (ML) model evaluation is a crucial step in the process of developing machine learning models. It involves assessing the performance of a model using a certain set of metrics to understand its effectiveness and accuracy. The aim of ML model evaluation is to determine how well a model is performing with regard to its ability to predict outcomes based on the input data it’s provided.

While ML model evaluation is especially important due to initial development of ML models, it should be a continuous process that continues throughout their deployment. The goal is to constantly improve the model’s performance by identifying and rectifying any shortcomings. It’s a cyclical process of training the model, evaluating its performance, fine-tuning it, and then re-evaluating it.

The methods used for ML model evaluation will depend on the type of machine learning algorithm used and the specific problem the model is attempting to solve. For example, a regression model might be evaluated using metrics such as mean squared error (MSE) or root mean squared error (RMSE), while a classification model might be evaluated using metrics such as accuracy, precision, recall, or F1 score. Regardless of the specific metrics used, the goal is always to evaluate the model in a way that provides meaningful insights into its performance and potential areas for improvement.

Why Is Model Evaluation Important?

Model evaluation is paramount to the success of any machine learning project. Without it, we would have no way of knowing how well our models are performing and whether they’re improving or degrading over time.

Here are the main reasons model evaluation is so important in machine learning:

  • Measures how well a model can deliver desired outcomes: By evaluating our models, we can see how close the model’s predictions are to the actual outcomes, which gives us an idea of how well the model is performing.
  • Identifies overfitting and underfitting: Overfitting occurs when a model is too complex and learns the noise in the training data, leading to poor performance on unseen data. Underfitting happens when the model is too simple to capture the underlying pattern in the data. Both of these issues can be identified through model evaluation.
  • Guides model selection: Most machine learning projects evaluate multiple models using different algorithms and hyperparameters. Model evaluation compares the performance of these models side by side, making it possible to select the best model for a specific problem.

11 Model Evaluation Metrics You Should Know

Here are some commonly used model evaluation metrics that can be used for different types of machine learning models:

Classification Metrics

1. Accuracy

Accuracy is one of the most intuitive and commonly used metrics in machine learning, particularly for classification problems. It measures the proportion of correct predictions (both true positives and true negatives) out of the total number of cases examined. To calculate accuracy, you divide the number of correct predictions by the total number of predictions.

High accuracy indicates that the model is effective at classifying the data correctly. However, it’s important to note that accuracy may not always be a reliable indicator of the performance of a model, particularly in cases where the class distribution is imbalanced. In such scenarios, a model might have a high accuracy by predominantly predicting the majority class, while failing to accurately identify instances of the minority class.

2. Precision

Precision is a key metric in classification problems, especially where false positives have significant consequences. It measures the proportion of correctly predicted positive observations to the total predicted positives.

High precision indicates that an algorithm returned substantially more relevant results than irrelevant ones. This metric is particularly crucial in scenarios like fraud detection, where falsely identifying a condition or fraud can have serious implications.

3. Recall

Recall, also known as sensitivity, measures the proportion of actual positives that were correctly identified. It is crucial in scenarios where missing a positive is more critical than falsely identifying a negative.

For example, in medical diagnosis, a high recall rate means most cancerous cases are correctly identified, reducing the risk of missing a diagnosis. In contrast to precision, recall emphasizes reducing false negatives.

4. F1 Score

The F1 Score is the harmonic mean of precision and recall, providing a balance between these two metrics. It is a useful measure when seeking a balance between precision and recall, especially when the class distribution is uneven.

An F1 Score reaches its best value at 1 (perfect precision and recall) and worst at 0. It is particularly useful when you need to compare two or more models that might have similar accuracy but differ in precision and recall.

5. ROC Curve and AUC

The Receiver Operating Characteristics (ROC) curve and the Area Under the Curve (AUC) are popular metrics used for evaluating the performance of binary classification models. The ROC curve is a plot that illustrates the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The AUC represents the degree or measure of separability, telling us how much a model is capable of distinguishing between classes.

ROC Curve diagram

The ROC curve is a valuable tool as it gives us a comprehensive view of the trade-off between the true positive rate and the false positive rate. It helps us to choose the optimal point that balances sensitivity (TPR) and specificity (1-FPR). An AUC close to 1 indicates that the model has a good measure of separability and is capable of distinguishing between positive and negative classes effectively.

Regression Metrics

6. Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is a measure used to quantify the average magnitude of errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation, where all individual differences have equal weight.

MAE is a linear score, which means all individual differences are weighted equally in the average. It is less sensitive to outliers compared to other metrics like Mean Squared Error (MSE). A lower MAE indicates a better fit of the model to data.

7. Mean Squared Error (MSE)

Mean Squared Error (MSE) is another popular metric used to measure the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. It is a widely used metric for regression problems.

MSE is more popular than MAE because it diminishes the effect of larger errors, which tends to be useful in the real world. The squaring of the error terms also has the effect of heavily weighting outliers in the calculation. A model with a low MSE value indicates a better fit to the data.

8. R² (Percentage of Variance Explained)

In the context of regression analysis, the percentage of variance explained is a statistical measure that describes the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It provides an indication of the goodness of fit of the model and is often denoted by R².

R² values range from 0 to 1, with 1 indicating that the independent variables perfectly predict the dependent variable. A high R² score signifies that the model explains a large portion of the variance in the response variable.

Natural Language Processing (NLP) Metrics

9. BLEU Score

In the domain of natural language processing, the Bilingual Evaluation Understudy (BLEU) score is used to measure the quality of machine-generated text such as translations. It compares the machine-generated text with one or more human-generated reference texts.

The BLEU score ranges from 0 to 1. A score of 1 indicates that the machine-generated text matches the human reference text perfectly. The closer the score is to 1, the more the machine translation resembles the reference human translation.

10. ROUGE Score

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score is a set of metrics used to evaluate automatic summarization and machine translation in NLP. It compares the overlap of n-grams, word sequences, and word pair matches between the machine-generated output and reference texts. This metric is essential in tasks like text summarization, where capturing the gist accurately is crucial.

11. Perplexity

Perplexity is a measurement in NLP used to quantify how well a probability model predicts a sample. A lower perplexity score indicates the model is better at making predictions. It’s often used in language modeling to assess the likelihood of a language model in generating a text similar to the training data. In other words, it measures the uncertainty of a language model in predicting new text data.

Learn more in our detailed guide to NLP testing

ML Model Evaluation: Sampling Techniques

In machine learning, model evaluation is the procedure of assessing how well a particular machine learning model performs on a given dataset. Here are a few common ways to sample a dataset in order to evaluate a model’s performance. These techniques can be applied to any evaluation metrics.

Train-Test Splitting

This is the simplest and most commonly used method for evaluating machine learning models. It involves splitting the dataset into a training set and a test set. The model is trained on the training set and evaluated on the test set.

The advantage of the train-test splitting method is its simplicity and speed. However, the downside is that the performance of the model can be heavily dependent on how the data is split. If the split is not representative of the overall dataset, the model’s performance can be significantly affected.


In cross-validation, the dataset is divided into ‘k’ subsets. The model is trained on ‘k-1’ subsets, and the remaining subset is used as the test set. This process is repeated ‘k’ times, with each subset serving as the test set once. The performance of the model is then averaged across the ‘k’ iterations.

Cross-validation provides a more robust measure of the model’s performance as it reduces the bias associated with the holdout method. However, it is more computationally intensive as the model has to be trained ‘k’ times.

Leave-One-Out Cross-Validation

Leave-one-out cross-validation (LOOCV) is a special case of cross-validation, where ‘k’ equals the number of observations in the dataset. In other words, in each iteration, the model is trained on all data points except one, which is used as the test set.

Although LOOCV provides the most unbiased estimate of the model’s performance, it is extremely computationally expensive, especially for large datasets.


Unlike the previous techniques that rely on partitioning the data into training and test sets, bootstrapping involves sampling with replacement from the original dataset to generate ‘n’ bootstrap samples. The model is then trained on each bootstrap sample and evaluated on the remaining observations.

Bootstrapping provides a measure of uncertainty around the model’s performance. However, it assumes that the original dataset is a good representation of the population, which might not always be the case.

Best Practices for ML Model Evaluation

While the techniques discussed above provide a way to measure a model’s performance, they are not sufficient on their own. It’s essential to follow a set of best practices during the evaluation process to ensure the model’s robustness and applicability.

Understand the Domain and Data

The domain refers to the field or industry in which you are working, and understanding it can provide valuable insights into the problem you are trying to solve. For example, in healthcare, certain symptoms might be more indicative of a disease than others, and understanding these relationships can help you develop a more effective model. This is why in this domain, data scientists should work closely with doctors and researchers.

Understanding your data is equally important. This involves knowing what the data represents, what each feature means, how the data was collected, and what kind of biases might be present. It also involves understanding the distribution of your data, as this can greatly affect the performance of your model. For example, if your data is imbalanced, certain performance metrics might be misleading, and you might need to use different techniques to train and evaluate your model.

Beware of Overfitting

Overfitting is a common problem in machine learning where a model performs well on the training data but poorly on unseen data. This usually happens when the model is too complex and learns the noise in the training data instead of the underlying pattern. Overfitting leads to models that are not generalizable and that perform poorly in real-world scenarios.

The sampling techniques we discussed above can help avoid overfitting. Other ways to tackle the problem include:

  • Regularization: Adding a penalty term to the loss function to prevent the coefficients from becoming too large, which can lead to overfitting.
  • Early stopping: Stopping the training process before the model starts to overfit.

Test on Independent Datasets

It’s important to test your model on independent datasets. These are datasets that were not used in any way during the model training process. Testing your model on independent datasets gives you a better idea of how your model will perform in the real world.

Independent datasets should ideally come from the same distribution as your training data. However, it can also be useful to test your model on data from slightly different distributions, as this can give you an idea of how robust your model is to changes in the data.

Fairness and Bias Evaluation

Bias in machine learning models can lead to unfair outcomes and can harm certain groups of people. For example, a hiring model that is biased against a certain race or gender can lead to unfair hiring practices.

Evaluating your model for fairness involves assessing how it performs for different groups of people. If your model performs significantly worse for one group compared to another, this could be a sign of bias. Techniques to mitigate bias include pre-processing the data to remove bias, modifying the learning algorithm to be fair, and post-processing the model outputs to ensure fairness.

Monitor Models Continuously

It’s important to monitor your models continuously. This involves tracking their performance over time and updating them as necessary. The performance of models can degrade over time due to changes in the data, changes in the underlying relationships, and other factors. By monitoring your models, you can catch these changes early and take action to update or retrain your models.

Monitoring can also help you catch errors and unexpected behavior in your models. For example, if the performance of your model suddenly drops, this could be a sign there is a sudden shift in real-world data, and the model cannot correctly analyze it based on its training dataset.

Automate the Evaluation Process

Given the iterative nature of the model development process, automating the evaluation process can save a lot of time and effort. This can involve creating scripts to train and evaluate models, setting up scheduled jobs to run these scripts, and creating dashboards to monitor the results. Dedicated AI testing and evaluation tools can make this process much easier.

Automation helps ensure consistency in the evaluation process. By automating the process, you can ensure that the same steps are followed every time, which makes the results more reliable and easier to compare.

Testing and Evaluating ML Models with Kolena

We built Kolena to make robust and systematic ML testing easy and accessible for all organizations. With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes not weeks. Kolena’s AI / ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.

Among its many capabilities, Kolena also helps with feature importance evaluation, and allows auto-tagging features. It can also display the distribution of various features in your datasets.

Reach out to us to learn how the Kolena platform can help build a culture of AI quality for your team.