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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -9,7 +9,7 @@ This example shows a simple way of leveraging some of the most widely used Machi
<div align="center">

<a href="">[![Static Badge](https://img.shields.io/badge/cartesi--rollups-1.0.0-5bd1d7)](https://docs.cartesi.io/cartesi-rollups/)</a>
<a href="">[![Static Badge](https://img.shields.io/badge/cartesi-cli-0.9.5-blue)](https://docs.sunodo.io/guide/introduction/what-is-sunodo)</a>
<a href="">[![Static Badge](https://img.shields.io/badge/docs-sunodo.io-blue)](https://docs.sunodo.io/guide/introduction/what-is-sunodo)</a>
<a href="">[![Static Badge](https://img.shields.io/badge/python-3.11-yellow)](https://www.python.org/)</a>
</div>

Expand Down Expand Up @@ -115,4 +115,4 @@ To change those, open the file `m2cgen/model/build_model.py` and change the foll
- `model`: defines the scikit-learn predictor algorithm to use. While it currently uses `sklearn.linear_model.LogisticRegression`, many [other possibilities](https://scikit-learn.org/stable/modules/classes.html) are available, from several types of linear regressions to solutions such as support vector machines (SVMs).
- `train_csv`: a URL or file path to a CSV file containing the dataset. It should contain a first row with the feature names, followed by the data.
- `include`: an optional list indicating a subset of the dataset's features to be used in the prediction model.
- `dependent_var`: the feature to be predicted, such as the entry's classification
- `dependent_var`: the feature to be predicted, such as the entry's classification