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# Classification Accuracy Evaluation
This is a flow illustrating how to evaluate the performance of a classification system. It involves comparing each prediction to the groundtruth and assigns a "Correct" or "Incorrect" grade, and aggregating the results to produce metrics such as accuracy, which reflects how good the system is at classifying the data.
Tools used in this flow
- `python` tool
## What you will learn
In this flow, you will learn
- how to compose a point based evaluation flow, where you can calculate point-wise metrics.
- the way to log metrics. use `from promptflow.core import log_metric`
- see file [calculate_accuracy.py](calculate_accuracy.py)
### 0. Setup connection
Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
```bash
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
```
### 1. Test flow/node
```bash
# test with default input value in flow.dag.yaml
pf flow test --flow .
# test with flow inputs
pf flow test --flow . --inputs groundtruth=APP prediction=APP
# test node with inputs
pf flow test --flow . --node grade --inputs groundtruth=groundtruth prediction=prediction
```
### 2. create flow run with multi line data
There are two ways to evaluate an classification flow.
```bash
pf run create --flow . --data ./data.jsonl --column-mapping groundtruth='${data.groundtruth}' prediction='${data.prediction}' --stream
```
You can also skip providing `column-mapping` if provided data has same column name as the flow.
Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
### 3. create run against other flow run
Learn more in [web-classification](../../standard/web-classification/README.md)