Accuracy is one of the most widely used metrics for evaluating machine learning models, especially for classification tasks. While it’s simple to use, accuracy is not always a reliable indicator of model performance. So, if you want to know when to use accuracy and when not, this article is for you. In this article, I’ll take you through a guide to accuracy in machine learning.
A Guide to Accuracy in Machine Learning
To understand everything about accuracy in machine learning, we’ll delve into three essential aspects of accuracy that every machine learning practitioner should understand:
- When and How Accuracy Shows Overfitting and Underfitting?
- When to Believe in Accuracy and When Not to Use It?
- How Much Accuracy is Enough?
So, let’s break down these essential aspects of accuracy in machine learning one by one.
When and How Accuracy Shows Overfitting and Underfitting
High Training Accuracy + Low Testing Accuracy: Indicates overfitting!
Overfitting occurs when a model performs exceptionally well on the training data but fails to generalize on unseen data. In this case, high accuracy on the training set might deceive you into believing the model is robust. However, the accuracy of the validation or test set will reveal the true story.
For example, you train a decision tree on a dataset with 95% training accuracy but only achieve 70% validation accuracy. This gap suggests the model has memorized the training data rather than learning generalizable patterns.
Similarly, Low Training Accuracy + Low Testing Accuracy: Indicates underfitting!
Underfitting happens when a model is too simple to capture the underlying patterns in the data, which results in poor performance on both the training and validation sets.
When to Believe in Accuracy and When Not to Use It
Accuracy is reliable when the dataset is balanced (i.e., all classes have roughly equal representation) and when false positives and false negatives have similar consequences.
For example, in spam email classification, where the dataset has equal numbers of spam and non-spam emails, accuracy will provide a good measure of the model’s performance.
Accuracy becomes problematic in the case of Imbalanced Datasets. For example, a medical diagnosis model where only 1% of patients have a rare disease. A naive model that always predicts no disease will have 99% accuracy but zero utility.
Use Precision, Recall, F1-score, or AUC-ROC for imbalanced datasets.
Metrics like Precision (focus on relevant positive predictions) and Recall (ability to identify all actual positives) provide a clearer view of model performance for minority classes. The F1-score balances Precision and Recall, while AUC-ROC evaluates the model’s ability to distinguish between classes across thresholds, which makes these metrics more effective for imbalanced scenarios.
How Much Accuracy is Enough?
The required level of accuracy depends on the application, the domain, and the tolerance for errors.
For Critical Applications (High Accuracy Required) aim for near-perfect accuracy (e.g., 99%+) with high reliability on all metrics, including Recall and Precision. Examples include Autonomous vehicles, medical diagnostics, and financial systems where even minor errors can have severe consequences.
Non-Critical Applications (Good-Enough Accuracy) accuracy of 70%-80% might be enough if it aligns with business goals. Examples include recommendation systems or marketing campaigns where some errors are tolerable since they don’t have critical consequences.
Summary
So, while accuracy is a simple and commonly used metric, it is essential to understand its limitations and context-specific relevance. Overfitting and underfitting can distort accuracy, imbalanced datasets can render it misleading, and the acceptable level of accuracy varies by application.
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