Accuracy:
Accuracy is a statistical measure that determines how close a measured value is to the true or desired value. It refers to the correctness of a prediction or measurement and is often expressed as a percentage. In the context of machine learning, accuracy is used to assess the performance of a classification model by comparing the number of correct predictions to the total number of predictions made.

Precision:
Precision, on the other hand, is a measure of how precise or exact a set of measurements or predictions are. It focuses on the proportion of true positive predictions out of all positive predictions made by a model. Precision is particularly relevant in situations where the cost of false positives is high, as it emphasizes the reduction of false positives to minimize errors.

Differences between Accuracy and Precision:

1. Definition:
- Accuracy measures the overall correctness of predictions or measurements.
- Precision focuses on the exactness or precision of positive predictions.

2. Calculation:
- Accuracy is calculated by dividing the number of correct predictions by the total number of predictions made and multiplying by 100 to obtain a percentage.
- Precision is calculated by dividing the number of true positive predictions by the sum of true positive and false positive predictions.

3. Interpretation:
- Accuracy provides a general assessment of model performance but may not be suitable when the dataset is imbalanced or when false positives/negatives have different consequences.
- Precision is useful when the focus is on minimizing false positives as it measures the proportion of correctly predicted positive instances.

4. Application:
- Accuracy is commonly used as a performance metric for classification models in situations where the distribution of classes is balanced or when false positives/negatives have equal importance.
- Precision is more applicable in scenarios where the cost of false positives is high, such as in medical diagnoses or spam detection, where minimizing false positives is crucial.

5. Trade-offs:
- Increasing accuracy can be achieved by improving both true positives and true negatives.
- Improving precision requires reducing false positives while maintaining true positives.

In conclusion, accuracy and precision are both important measures in assessing the performance of models or measurements. While accuracy provides a broad evaluation of correctness, precision focuses on the exactness of positive predictions, making it particularly relevant in situations where false positives have significant consequences.