Text Classification Basics Part 2: Model Evaluation

Chitra's Playground
2 min readSep 25, 2024

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After processing data through a machine learning model, it’s crucial to evaluate its performance using appropriate metrics. The most commonly used evaluation metrics are accuracy, recall, precision, and the F1-score.

Accuracy

Accuracy measures the proportion of correct predictions made by the model out of the total predictions. For example, if our model correctly predicts 90 out of 100 items in the test set, the accuracy is 90/100, or 90%. Accuracy works well when the dataset is balanced, meaning the number of items in each class (e.g., True and False) is roughly equal, such as 50 in the True class and 50 in the False class. However, accuracy can be misleading with an imbalanced dataset, where one class significantly outnumbers the other (e.g., 99 True and 1 False). In such cases, accuracy may not accurately reflect model performance, and we should consider other metrics like recall and precision.

Recall

Recall measures the model’s ability to identify all relevant cases in the dataset. It is calculated as the ratio of true positives (correctly predicted relevant cases) to the sum of true positives and false negatives (relevant cases the model missed). A high recall indicates that the model is good at finding all relevant instances, but it doesn’t guarantee that the model is precise.

Precision

Precision measures the proportion of correctly predicted relevant cases out of all cases the model predicted as relevant. It is the ratio of true positives to the sum of true positives and false positives (cases the model wrongly identified as relevant). A high precision means the model is very selective in its predictions, reducing false positives, but it doesn’t necessarily capture all relevant instances.

F1-Score

The F1-score provides a balance between precision and recall. It’s especially useful when we need to strike an optimal balance between the two, particularly in cases of imbalanced data. The F1-score is the harmonic mean of precision and recall, and it is calculated using the following equation:

F1 score formula

In summary, while accuracy is often the go-to metric, it’s essential to consider recall, precision, and the F1-score especially when dealing with imbalanced datasets to get a more complete understanding of your model’s performance.

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Chitra's Playground
Chitra's Playground

Written by Chitra's Playground

Tech enthusiast with a passion for machine learning & eating chicken. Sharing insights on my learning journey.

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