# Examples ## Sample notebooks Here you can find simple examples on toy datasets to quickly explore what Evidently can do right out of the box. Each example shows how to create a default Evidently dashboard, a JSON profile and an HTML report. | Report | Jupyter notebook | Colab notebook | Data source | | ----------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | Data Drift + Categorical Target Drift (Multiclass) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/multiclass_target_and_data_drift_iris.ipynb) | [link](https://colab.research.google.com/drive/1Dd6ZzIgeBYkD_4bqWZ0RAdUpCU0b6Y6H) | Iris plants sklearn.datasets | | Data Drift + Categorical Target Drift (Binary) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/binary_target_and_data_drift_breast_cancer.ipynb) | [link](https://colab.research.google.com/drive/1gpzNuFbhoGc4-DLAPMJofQXrsX7Sqsl5) | Breast cancer sklearn.datasets | | Data Drift + Numerical Target Drift | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/numerical_target_and_data_drift_california_housing.ipynb) | [link](https://colab.research.google.com/drive/1TGt-0rA7MiXsxwtKB4eaAGIUwnuZtyxc) | California housing sklearn.datasets | | Regression Performance | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/regression_performance_bike_sharing_demand.ipynb) | [link](https://colab.research.google.com/drive/1ONgyDXKMFyt9IYUwLpvfxz9VIZHw-qBJ) | Bike sharing UCI: [link](https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset) | | Classification Performance (Multiclass) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/classification_performance_multiclass_iris.ipynb) | [link](https://colab.research.google.com/drive/1pnYbVJEHBqvVmHUXzG-kw-Fr6PqhzRg3) | Iris plants sklearn.datasets | | Probabilistic Classification Performance (Multiclass) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/probabilistic_classification_performance_multiclass_iris.ipynb) | [link](https://colab.research.google.com/drive/1UkFaBqOzBseB_UqisvNbsh9hX5w3dpYS) | Iris plants sklearn.datasets | | Classification Performance (Binary) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/classification_performance_breast_cancer.ipynb) | [link](https://colab.research.google.com/drive/1b2kTLUIVJkKJybYeD3ZjpaREr_9dDTpz) | Breast cancer sklearn.datasets | | Probabilistic Classification Performance (Binary) | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/probabilistic_classification_performance_breast_cancer.ipynb) | [link](https://colab.research.google.com/drive/1sE2H4mFSgtNe34JZMAeC3eLntid6oe1g) | Breast cancer sklearn.datasets | | Data Quality | [link](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/data_quality_bike_sharing_demand.ipynb) | [link](https://colab.research.google.com/drive/1XDxs4k2wNHU9Xbxb9WI2rOgMkZFavyRd) | Bike sharing UCI: [link](https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset) | ## Tutorials To better understand potential use cases for Evidently (such as model evaluation and monitoring), refer to the **detailed tutorials** accompanied by the blog posts. | Title | Jupyter notebook | Colab notebook | Blog post | Data source | | ---------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | | Monitor production model decay | [link](https://github.com/evidentlyai/evidently/blob/main/examples/data_stories/bicycle_demand_monitoring.ipynb) | [link](https://colab.research.google.com/drive/1xjAGInfh_LDenTxxTflazsKJp_YKmUiD) | [How to break a model in 20 days. A tutorial on production model analytics.](https://evidentlyai.com/blog/tutorial-1-model-analytics-in-production) | Bike sharing UCI: [link](https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset) | | Compare two models before deployment | [link](https://github.com/evidentlyai/evidently/blob/main/examples/data_stories/ibm_hr_attrition_model_validation.ipynb) | [link](https://colab.research.google.com/drive/12AyNh3RLSEchNx5_V-aFJ1_EnLIKkDfr) | [What Is Your Model Hiding? A Tutorial on Evaluating ML Models.](https://evidentlyai.com/blog/tutorial-2-model-evaluation-hr-attrition) | HR Employee Attrition: [link](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) | | Evaluate and visualize historical drift | [link](https://github.com/evidentlyai/evidently/blob/main/examples/integrations/mlflow_logging/historical_drift_visualization.ipynb) | [link](https://colab.research.google.com/drive/12AyNh3RLSEchNx5_V-aFJ1_EnLIKkDfr) | [How to detect, evaluate and visualize historical drifts in the data.](https://evidentlyai.com/blog/tutorial-3-historical-data-drift) | Bike sharing UCI: [link](https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset) | | Create a custom report (tab) with PSI widget for drift detection | [link](https://github.com/evidentlyai/evidently/blob/main/examples/data_stories/california_housing_custom_PSI_widget_and_tab.ipynb) | [link](https://colab.research.google.com/drive/1FuXId8p-lCP9Ho_gHeqxAdoxHRuvY9d0) | --- | California housing sklearn.datasets | ## Integrations To see how to integrate Evidently in your prediction pipelines and use it with other tools, refer to the **integrations**. {% content-ref url="/pages/uX0Hb8Y0B1chT34jNeBT" %} [Integrations](/v0.1.57/integrations.md) {% endcontent-ref %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs-old.evidentlyai.com/v0.1.57/examples.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.