Highaccuracy

High accuracy is the degree to which a measurement, estimate, or model output approaches the true value. It is commonly contrasted with precision, which describes repeatability or consistency rather than closeness to the true value. In practice, accuracy is assessed by comparing results to a known reference or to accepted standards, using metrics such as absolute or relative error, RMSE, or MAE. In metrology, accuracy is supported by calibration and traceability to national or international standards, and it is accompanied by an uncertainty budget that accounts for all significant sources of error.

In geospatial and navigation contexts, high accuracy refers to positioning and timing results that closely match real-world locations. Techniques such as differential corrections, real-time kinematic (RTK) positioning, and precise point positioning are employed to reduce error and achieve centimeter- to meter-level accuracy under suitable conditions. In these domains, accuracy is highly dependent on data quality, environmental conditions, and the integrity of reference measurements.

In machine learning and data analysis, high accuracy describes a model’s correct predictions on a given dataset. It is important to consider dataset quality, class balance, and the potential for overfitting. A model with high accuracy on training data may not generalize well to new data, so robust evaluation across multiple datasets and metrics, along with calibration and uncertainty assessment, is essential.

The term does not imply a universal threshold; what constitutes “high accuracy” varies by domain, method, and available reference.