Feat/classification example (#2871)

* add classifier example notebook

* add classifier example notebook

* update changelog

* add example to tests and docs

Author: dennisbader

.github/workflows/merge.yml

@@ -85,7 +85,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb]
+        example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb, 24-SKLearnClassifierModel-examples.ipynb]
     steps:
       - name: "Clone repository"
         uses: actions/checkout@v4

CHANGELOG.md

@@ -11,11 +11,22 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
-- Added support for classification forecasting with SKLearn-like forecasting models. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
- - Added `SklearnClassifierModel` which can take any sklearn-like classifier model.
- - Added `LightGBMClassifierModel`, `XGBClassifierModel` and `CatBoostClassifierModel` which use the classifier models of the respective libraries.
- - Added `ClassProbabilityLikelihood` and set it as the default likelihood for classifiers to predict class probabilities with `predict_likelihood_parameters=True` when calling `predict()`.
-- Added classification metrics `accuracy()`, `f1()`, `precision()`, and `recall()`, `confusion_matrix()` to the `metrics` module. Use these metrics to evaluate the performance of classification models. [#2767](https://github.com/unit8co/darts/pull/2767) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
+- 🚀🚀 Added the first **classification forecasting models** for time series labeling or forecasting future class labels. Check out [this new notebook](https://unit8co.github.io/darts/examples/24-SKLearnClassifierModel-examples.html) that demonstrates all added capabilities on the example of time series labeling. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
+  - All models come with the following support:
+    - Uni- and multivariate classification.
+    - Deterministic and probabilistic (predicted class probabilities and / or sampled class labels) forecasting.
+    - Past, future, and static covariates.
+  - `SklearnClassifierModel`: A wrapper around any scikit-learn-like classifier (default: Logistic Regression)
+  - `CatBoostClassifierModel`: Wrapper around CatBoost's CatBoostClassifier with native categorical feature support
+  - `LightGBMClassifierModel`: Wrapper around LightGBM's LGBMClassifier with native categorical feature support.
+  - `XGBClassifierModel`: Wrapper around XGBoost's XGBClassifier
+- Added the first **classification metrics**: Use these metrics to evaluate the performance of the new classification forecasting models. Check out the metrics' documentation for more information on how to extract label-specific metrics, customize aggregation methods, and more. [#2767](https://github.com/unit8co/darts/pull/2767) by [Jonas Blanc](https://github.com/jonasblanc) and [Dennis Bader](https://github.com/dennisbader).
+  - `accuracy()`: Accuracy
+  - `precision()`: Precision
+  - `recall()`: Recall
+  - `f1()`: F1 score
+  - `confusion_matrix()`: Confusion Matrix
+- Added [an example notebook](https://unit8co.github.io/darts/examples/24-SKLearnClassifierModel-examples.html) that demonstrates how to use and evaluate the new classification models. [#2871](https://github.com/unit8co/darts/pull/2871) by [Dennis Bader](https://github.com/dennisbader).
 
 **Fixed**
 
@@ -30,14 +41,15 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Dependencies**
 
 - Removed the upper version cap on `scipy<1.16.0` since `statsmodels` added support in version `0.14.5`. [#2853](https://github.com/unit8co/darts/pull/2853) by [Dennis Bader](https://github.com/dennisbader).
-- We set an upper version cap on `pytorch-lightning<2.5.3` until an issue with `lr_find()` is resolved. [#2871](https://github.com/unit8co/darts/pull/2871) by [Dennis Bader](https://github.com/dennisbader).
+- We set an upper version cap on `pytorch-lightning<2.5.3` until an issue with `lr_find()` is resolved. [#2872](https://github.com/unit8co/darts/pull/2872) by [Dennis Bader](https://github.com/dennisbader).
 
 ### For developers of the library:
 
+- Added `ClassProbabilityLikelihood` and set it as the default likelihood for classifiers to predict class probabilities with `predict_likelihood_parameters=True` when calling `predict()`. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc)
 - Renamed `RegressionModelWithCategoricalCovariates` to `RegressionModelWithCategoricalFeatures` which now also supports categorical target features. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc)
 - Added `MultiOutputClassifier` for handling multi-output classification tasks. [#2765](https://github.com/unit8co/darts/pull/2765) by [Jonas Blanc](https://github.com/jonasblanc)
 - Cleaned up `Dockerfile` to only include the necessary files for building the Darts package. [#2854](https://github.com/unit8co/darts/pull/2854) by [Dennis Bader](https://github.com/dennisbader).
-- Added `ElectricityConsumptionZurichDataset` to the repository instead of downloading it from source to fix failing tests due to continuously updating source data. [#2862](https://github.com/unit8co/darts/pull/2862) and [#2862](https://github.com/unit8co/darts/pull/2863) by [Dennis Bader](https://github.com/dennisbader).
+- Added `ElectricityConsumptionZurichDataset` to the repository instead of downloading it from source to fix failing tests due to continuously updating source data. [#2862](https://github.com/unit8co/darts/pull/2862) and [#2863](https://github.com/unit8co/darts/pull/2863) by [Dennis Bader](https://github.com/dennisbader).
 
 ## [0.36.0](https://github.com/unit8co/darts/tree/0.36.0) (2025-06-29)
 

docs/source/examples.rst

@@ -86,6 +86,16 @@ Regression models example notebook:
 
    examples/20-SKLearnModel-examples.ipynb
 
+Classification Models
+=====================
+
+Classification models example notebook:
+
+.. toctree::
+   :maxdepth: 1
+
+   examples/24-SKLearnClassifierModel-examples.ipynb
+
 Conformal Prediction
 =================