Update quickstart for model documentation in Jupyter notebooks

validmind-library
2.8.26
enhancement
Published

June 26, 2025

We've improved our Jupyter notebooks to make the model documentation quickstart guide more user-friendly and informative for beginners. A new "quickstart" directory has been added to notebooks/, along with an updated README to guide you:

Screenshot 2025-05-14 at 11 29 11 AM

Screenshot 2025-05-14 at 11 29 11 AM

To get started with documenting models using the ValidMind Library, check out our updated Quickstart for model documentation notebook:

This update removes the old notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb file as the new file and directory replace it.

A webpage section titled Preprocessing the raw dataset includes instructions for preparing a dataset for ValidMind. The section is divided into two main parts: Split the dataset and Separate features and targets. Under Split the dataset, three bullet points explain the use of train_df, validation_df, and test_df for training, validation, and testing respectively, with a code snippet showing the command customer_churn.preprocess(raw_df). The Separate features and targets section describes the need to differentiate inputs and correct answers, with code snippets defining x_train, x_val, y_train, and y_val using the customer_churn.target_column variable. The layout uses a clear hierarchy with bold section titles and code snippets in a monospaced font.

A webpage section titled Training an XGBoost classifier model contains text and code snippets. The text explains the process of setting up early stopping to prevent overfitting and evaluating model performance using three metrics: error, logloss, and auc. Below the text, a code snippet demonstrates setting early stopping rounds to 10. Another code block shows setting evaluation metrics with model.set_params and a list of metrics. The final code snippet illustrates fitting the model using model.fit with parameters x_train, y_train, x_val, and y_val, and setting verbose to False. The layout is structured with explanatory text followed by corresponding code examples.