# Learn how to make positive and negative annotations

Positive and negative annotations play a critical role in training machine learning models.

Positive annotations help the model identify and learn the features associated with a particular input. Negative annotations, on the other hand, help the model distinguish between similar inputs by highlighting what the input is not.

Some inputs may also have overlapping features, especially in complex datasets. Providing both positive and negative annotations reduces the risk of the model misclassifying similar but distinct inputs, leading to more precise predictions.

By annotating your data with both positive and negative labels, you provide your model with the comprehensive information it needs to learn effectively, which results in better performance and more reliable outcomes.

## value
- The `value` parameter is used to specify the presence of a concept in a request. A `value` of 1.0 indicates that the concept is present (true), while a `value` of 0.0 indicates that the concept is not present (false).
- When making HTTP requests, if the `value` parameter is omitted, it defaults to a positive annotation. In contrast, for gRPC requests, a missing `value` is interpreted as a negative annotation. To avoid unintended behavior, it's recommended to always explicitly set the `value` parameter in your requests.

## Positive Annotation
If an input is tagged with a positive annotation, it is considered a positive label for it.

### Example for Python (gRPC)
```python
USER_ID = 'YOUR_USER_ID_HERE'
PAT = 'YOUR_PAT_HERE'
APP_ID = 'YOUR_APP_ID_HERE'
INPUT_ID = "53d0362a9dfa4e03b2293375e2d0db73"
CONCEPT_ID_1 = "tree"
CONCEPT_ID_2 = "water"

# Continue with implementation...
```

### Example for JavaScript (REST)
```javascript
const USER_ID = 'YOUR_USER_ID_HERE';
const PAT = 'YOUR_PAT_HERE';
const APP_ID = 'YOUR_APP_ID_HERE';
// Change these based on the image you want to annotate
const INPUT_ID = "c99f1b557d1d43d1916b46f8ce4a0487";
const CONCEPT_ID_1 = "tree";
const CONCEPT_ID_2 = "water";

// Continue with implementation...
```

## Negative Annotation
If an input is tagged with a negative annotation, it is treated as a negative label for that concept. It emphasizes that the input does not belong to that concept.

# Continue with implementation...
```

// Continue with implementation...
```