# bfcl / bfcl-simple-python-106

- taskset: [bfcl](https://harnessreport.com/tasks/bfcl.md)
- difficulty: medium
- category: function_calling
- language: 
- runnable from the site: no
- agent timeout: 300s

## Results by harness

_none yet_

## Instruction

```
# Task

Train a random forest classifier on dataset your_dataset_name with maximum depth of trees as 5, and number of estimators as 100.

## Available Functions

Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the functions can be used, do not invoke any function. If the given question lacks the parameters required by the function, do not invoke the function.

Here is a list of functions in JSON format that you can invoke.
[
    {
        "name": "train_random_forest_classifier",
        "description": "Train a Random Forest classifier with the specified parameters.",
        "parameters": {
            "type": "dict",
            "properties": {
                "dataset": {
                    "type": "string",
                    "description": "The dataset to train the classifier on."
                },
                "max_depth": {
                    "type": "integer",
                    "description": "The maximum depth of the trees in the forest."
                },
                "n_estimators": {
                    "type": "integer",
                    "description": "The number of trees in the forest."
                }
            },
            "required": [
                "dataset",
                "max_depth",
                "n_estimators"
            ]
        }
    }
]

## Output

Analyze the request and determine the appropriate function call(s). 
Write ONLY a JSON array to `/app/result.json`.

Format:
- If a function applies: `[{"function_name": {"param1": "value1"}}]`
- If no function applies: `[]`

Example:
```bash
echo '[{"get_weather": {"city": "NYC"}}]' > /app/result.json
```

IMPORTANT: You MUST execute the command to write the file.
```
---
Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp