Whether 80% accuracy is considered good in machine learning depends on the context of the problem and the baseline performance. In some domains, such as healthcare or autonomous driving, even small errors can have critical consequences, so higher accuracy (e.g., 95%+) may be required. On the other hand, for less critical tasks like product recommendations, 80% could be sufficient. Accuracy alone doesn’t always reflect model performance. For imbalanced datasets, accuracy might be misleading. For example, if only 5% of samples belong to the positive class, a model predicting all samples as negative would still achieve 95% accuracy. Metrics like precision, recall, F1-score, and AUC-ROC are often better indicators of performance in such cases. It’s also important to consider whether the model outperforms simpler baselines or existing methods. For example, if a problem already has a rule-based system achieving 75% accuracy, a machine learning model with 80% accuracy may not justify its complexity. However, if the baseline accuracy is 50% (random guessing), then 80% represents a significant improvement. Always evaluate model performance in the context of the task’s requirements and trade-offs.
Is 80% accuracy good in machine learning?
- Getting Started with Zilliz Cloud
- Vector Database 101: Everything You Need to Know
- The Definitive Guide to Building RAG Apps with LangChain
- Information Retrieval 101
- Advanced Techniques in Vector Database Management
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the concept of “open-book” QA and how does it relate to RAG? How would you evaluate an LLM in an open-book setting differently from a closed-book setting?
**Open-Book QA and Its Relation to RAG**
Open-book question answering (QA) refers to a scenario where a model can acces
How do MAS technologies integrate with IoT devices?
MAS (Multi-Agent Systems) technologies integrate with IoT (Internet of Things) devices by using autonomous agents that c
What is overfitting in reinforcement learning?
Overfitting in reinforcement learning refers to the situation where an agent learns a policy that performs well on the t