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jgiannuzzi authored Dec 11, 2023
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5 changes: 0 additions & 5 deletions docs/example/README.md

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8 changes: 2 additions & 6 deletions docs/example/pyproject.toml
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[tool.poetry]
name = "fast-track-test"
name = "fasttrackml-test"
version = "0.1.0"
description = "A Random Forest Classifier for EMG Data - Fast Track Test"
description = "Python Example for FastTrackML"
authors = ["Sharpz7 <[email protected]>"]

[tool.poetry.dependencies]
python = "^3.9"
pandas = "^1.5.3"
scikit-learn = "^1.2.1"
mlflow-skinny = "^2.6.0"
matplotlib = "^3.7.2"

[tool.poetry.dev-dependencies]

[build-system]
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101 changes: 0 additions & 101 deletions docs/example/random_forest.py

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17 changes: 0 additions & 17 deletions docs/quickstart.md
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Expand Up @@ -69,20 +69,3 @@ After running this script, you should see the following output from http://local
![FastTrackML UI](images/runs_ui.png)

From here you can check out the metrics and run information to see more details about the run.

## Testing a Random Forest Model

**Note that since artifacts are not yet supported, most of the autolog features will not work.**

### Get the required data

From Kaggle, download https://www.kaggle.com/datasets/kyr7plus/emg-4?resource=download

Extract the zip file and move the files to `docs/example/data`.

### Run the script

```bash
poetry run python3 random_forest.py
```

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