Whether 80% accuracy is good in machine learning depends heavily on the problem you’re solving, the baseline performance, and the cost of errors. For some tasks, 80% might be a strong result, while for others, it could indicate significant room for improvement. For example, in a binary classification problem where classes are evenly balanced (e.g., predicting whether an email is spam or not), 80% accuracy might be acceptable for a first iteration, especially if it outperforms a simple baseline like random guessing (50%). However, in a medical diagnosis scenario where missing a positive case (e.g., cancer detection) has severe consequences, 80% accuracy could be unacceptably low, even if it beats a naive baseline. Context is key.
The quality of the data and the difficulty of the task also matter. If your dataset is noisy, imbalanced, or lacks informative features, achieving 80% accuracy might represent a solid effort. For instance, in a sentiment analysis task with ambiguous or sarcastic text, 80% could be competitive given the inherent challenges. Conversely, if you’re working on a well-studied problem like MNIST digit classification, 80% accuracy would be far below state-of-the-art results (which often exceed 99%) and suggest issues in model design or training. Always compare your results to benchmarks for similar tasks to gauge performance. If no benchmarks exist, test simpler models (e.g., logistic regression) to establish a baseline—if they achieve 75% accuracy, an 80% result from a more complex model may not justify the added complexity.
Finally, consider the practical impact of the model. If deploying a model with 80% accuracy adds clear value (e.g., automating a manual process with tolerable error rates), it might be worth implementing while iterating for improvement. For example, a customer support chatbot that resolves 80% of routine queries could free up human agents for complex cases. However, if errors are costly (e.g., financial fraud detection), even a 5% error rate might be too high. In such cases, supplement accuracy with metrics like precision, recall, or F1-score to better understand failure modes. A model with 80% accuracy but 95% recall for critical cases might be preferable to one with higher overall accuracy but worse performance on high-stakes examples. Always align evaluation metrics with business or user needs.